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Computers in Human Behavior 136 (2022) 107404
Available online 15 July 2022
0747-5632/© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Glued to your phone? Generation Z’s smartphone addiction and online
compulsive buying
Michela Cesarina Mason
a
, Gioele Zamparo
a
, Andrea Marini
b
, Nisreen Ameen
c
,
*
a
University of Udine, Department of Economics and Statistics. Via Tomadini 30, Udine, 33100, Italy
b
University of Udine, Department of Languages and Literatures, Communication, Education and Society, Italy
c
School of Business and Management Royal Holloway, University of London, UK
ARTICLE INFO
Keywords:
Smartphone addiction
Generation Z
Compulsive buying
S–O-R
Flow experience
Mood regulation
ABSTRACT
Recent studies found that smartphone usage has become an addiction nowadays, especially among young con-
sumers. The abuse of these digital devices affects individuals’ social life and well-being. Of particular interest in
this regard is the study of compulsive buying, as it has been noted a possible co-occurrence of this disorder with
smartphone abusive tendencies. With a model theoretically anchored in the stimulus-organism-response
framework, the current study investigates the novel connection between smartphone addiction and online
compulsive buying in a sample of 275 Generation Z consumers. The proposed model integrates mood regulatory
behaviours and ow experiences associated with smartphone addiction to affect online compulsive buying be-
haviours. The current research contributes to the literature on compulsive buying behaviours and smartphone
addiction by offering empirical evidence that (1) smartphone addiction and online compulsive buying are
related; and (2) mood regulatory behaviours and ow experience act as strengthening factors in this relationship.
This article advances knowledge in terms of theory and practice on Generation Z consumers’ smartphone
addiction and online compulsive buying.
1. Introduction
The wide diffusion of mobile devices and smartphones has signi-
cantly changed various aspects of our lives (Ameen et al., 2020a).
Entertainment, work, social interactions and education are vehiculated
through the screen of a smartphone. Smartphones have modied the
spending habits of consumers to the extent that some insiders expect
mobile commerce sales to grow up to 700 billion dollars by 2025
(Bloomberg, 2021). Nonetheless, recent years have also witnessed the
offspring of new forms of mobile-related behavioural addictions, espe-
cially among young individuals, such as smartphone addiction (Olson
et al., 2022). Smartphone addiction is dened as the excessive use of
smartphones characterised by uncontrolled usage, neglect of daily ac-
tivities, and negative consequences for the user’s life (Otsuka et al.,
2022). It affects a person’s social, physical, and psychological func-
tioning as it may induce states of depression, stress, sleep disorders, pain
in the thumbs, decreased pinch strength, and reduced hand functions
(Bian & Leung, 2015; Lee et al., 2014; Samaha & Hawi, 2016; ˙
Inal et al.,
2015). It may co-occur with other behavioural addictions such as
compulsive buying (Jiang & Shi, 2016).
The potential link between compulsive buying and the online envi-
ronment has been explored in recent years by introducing the label
‘online compulsive buying’ (Zheng et al., 2020). Both traditional and
online compulsive buyers are characterised by a loss of spending control,
a feeling of gratication and release of tension following the purchase,
and a senseless and item-unrelated repetition of this dysfunctional
behaviour (Duroy et al., 2014; Müller & Mitchell, 2014). Lyons and
Henderson (2000, p.739) stated that online compulsive buying is “an old
problem in a new marketplace”. As smartphones offer access to the
internet, they may act as an enabler for online compulsive buying be-
haviours. These devices offer intriguing, stimulating and customised
digital environments (e.g., mobile shopping platforms). Furthermore,
they allow individuals to make purchases with unprecedented ease and
availability and avoid interaction with others (Kukar-Kinney et al.,
2016). Altogether, this may facilitate the emergence and consequent
satisfaction of those urges to purchase felt by compulsive buyers.
Smartphones are a fundamental tool for most people nowadays, and
young generations make the most intense use of and are strongly
* Corresponding author.
E-mail addresses: michela.mason@uniud.it (M.C. Mason), zamparo.gioele@spes.uniud.it (G. Zamparo), andrea.marini@uniud.it (A. Marini), nisreen.ameen@rhul.
ac.uk (N. Ameen).
Contents lists available at ScienceDirect
Computers in Human Behavior
journal homepage: www.elsevier.com/locate/comphumbeh
https://doi.org/10.1016/j.chb.2022.107404
Received 7 May 2022; Received in revised form 11 July 2022; Accepted 12 July 2022
Computers in Human Behavior 136 (2022) 107404
2
attached to these devices. Nevertheless, an individual’s excessive
attachment to their smartphone has also dark sides (Lee et al., 2014).
The diffusion of smartphones in people’s everyday lives has been par-
alleled by vast debate in the literature discussing the positive or negative
relationship between daily screen time and health and behavioural
outcomes (Ratan et al., 2021). However, much about this relationship
lies under-researched. There is no clear understanding of how mobile
devices may inuence the shopping process (Shankar et al., 2016) and if
- in some cases – may lead to dysfunctional shopping behaviours, such as
compulsive buying. Moreover, the pandemic substantially increased the
time people spent on their smartphones (Elhai et al., 2020; Serra et al.,
2021) and mobile shopping platforms (Chopdar et al., 2022). Thus, this
research aims at exploring the potential link between smartphone
addiction and compulsive buying behaviour. In particular, this research
focuses on generation Z (Gen Z) consumers who constitute the biggest
future challenge for marketing (Priporas et al., 2017) and represent over
one-third of the Global population (Schroders, 2021).
Thus, the current research contributes to the current body of
knowledge in three ways. First, it highlights a novel connection between
dysfunctional smartphone use in young individuals and the develop-
ment of compulsive buying tendencies. Second, it highlights the pivotal
role that the consumer’s internal states (i.e., mood regulation mecha-
nism and ow experience) play in the development of compulsive be-
haviours also in online environments. Third, this study contributes to the
existing literature on Gen Z behaviour by addressing a gap on the con-
sequences of one of their behavioural traits (i.e., smartphone addiction)
in terms of leading to addictive shopping behaviour. Despite the exis-
tence of previous studies on smartphone addiction (e.g., Hu et al., 2022;
Li et al., 2022; No¨
e et al., 2019; Olson et al., 2022), its impact on
compulsive buying behaviour has not been investigated before, partic-
ularly among Gen Z consumers.
Based on the stimulus-organism-response (S–O-R) model (Mehrabian
& Russell, 1974), this research proposes a new framework to explore the
relationship between online compulsive buying and smartphone
addiction in Gen Z consumers. In addition, serial mediation analysis was
used to prove the results empirically and, to overcome some of the
limitations related to symmetrical modelling, such as multiple regres-
sion analysis and structural equation modelling (Woodside, 2019),
fuzzy-set qualitative comparative analysis (fsQCA) was also employed to
highlight which causal congurations better explain high levels of on-
line compulsive buying.
The remainder of the paper is organised as follows. The next section
offers a comprehensive review of mood regulation and ow experience
concepts and their relation to smartphone addiction and online
compulsive buying. Section three describes the sample and the empirical
method. Section four includes the main results of the serial mediation
analysis and fsQCA. In Section ve, the insights obtained from the serial
mediation and fsQCA are discussed, and practical and theoretical im-
plications are highlighted. Finally, in section six, the study’s limitations
are addressed with considerations about future research.
2. Theoretical background
2.1. Generation Z
Gen Z comprises young adults born in 1995 or later (Fister-Gale,
2015) who have not experienced the world without digital technology
(Ameen & Anand, 2020; Ameen et al., 2021). These digitally native
individuals are tech-savvy, have grown up with high exposure to social
media and mobile technologies (Fister-Gale, 2015) and are born into a
VUCA (volatility, uncertainty, complexity, and ambiguity) world
(Casalegno et al., 2022). Wood (2013) and Priporas et al. (2017) explain
that four trends characterise Gen Z: (1) An interest in new technologies
(2) An insistence on ease of use (3) A desire to feel safe; and (4) A desire
to temporarily escape the realities they face. They have experienced a lot
in their brief lifetimes and have encountered political, social,
technological and economic changes. Gen Z as consumers are less loyal
to retailers, and they expect retailers to get the product to them, as a
consequence, retailers feel pressure to nd new ways to grab and hold
consumers’ attention (Priporas et al., 2017). They have higher expec-
tations, no brand loyalty and care more about the experience (Schloss-
berg, 2016). However, these individuals are considered the key
generation to lead m-commerce consumer behaviour in the upcoming
years (Monaco, 2018). Thus, studying Gen Z consumers is utterly valu-
able from a marketing perspective. Some estimates afrm that the
cohort has $360 billion in disposable income (Pollard, 2021) and is
destined to grow steadily in the future.
Gen Zs are constantly connected and prefer communication via
technology rather than direct contact (Pol´
akov´
a & Klímov´
a, 2019).
Nevertheless, technology does not only shape Gen Z’s social lives but
other aspects, including their physical well-being, learning processes,
and social and professional identities. Furthermore, because of their
dramatic exposure to global mass media, popular culture and interna-
tional companies’ marketing activities, they share a common consumer
culture and traits (Benasso & Cuzzocrea, 2019; Ng et al., 2019). Their
lives are characterised by multiple information ows and
frequent-and-fast interactions with content and people. Most of these
ows of information are vehiculated through a smartphone. Coherently
with the conceptualization of smartphones as a ‘package of stimuli’
proposed in this research, some authors state that smartphones are,
indeed, an “everything hub” for Gen Z including watching a TV show,
listening to a song, chatting with a friend, playing a videogame all can be
done on one’s phone (Turner, 2015). Not surprisingly, it is estimated
that the average Gen Z individual spends 4:15 h per day looking at
his/her smartphone screen. (GlobalWebIndex, 2019). Their dependence
on smartphones also has led some authors (Bragazzi & Del Puente, 2014)
to propose the inclusion of nomophobia (i.e., the fear of being without a
smartphone) in the new Diagnostic and Statistical Manual of Mental
Disorders (DSM). Research addressing this topic has highlighted that the
strong attachment of Gen Z towards mobiles may be a way to cope with
loneliness (Gentina & Chen, 2019), stress, anxiety (Vahedi & Saiphoo,
2018), and that smartphone addiction may also be related to escapism
(Wang et al., 2015). These devices, for Gen Z, are an integral part of their
daily lives and represent a social hub that provides them with inspira-
tion, interactivity, and creativity. However, researchers have also
highlighted the downsize of Gen Zs’ overattachment, namely the abuse
of mobile devices has been connected in Gen Z to a lack of physical
activity, reduced sleep quality (Zhai et al., 2020), academic cheating
(Gentina et al., 2018), depression (Stankovi´
c et al., 2021) and reduced
attention to external stimuli (Mourra et al., 2020).
Smartphones also affect Gen Zs’ shopping behaviours. This cohort
makes the most intensive use of mobile shopping apps (AppAnnie,
2020), and the pandemic may have further reinforced this trend.
Research addressing this mainly focuses on this generation’s expecta-
tions and attitudes toward mobile shopping (Goldring & Azab, 2021;
Lissitsa & Kol, 2021), while there is a paucity of studies addressing the
potential downsides of mobile shopping in Gen Z. Several studies have
found a negative correlation between compulsive buying and age,
afrming that young consumers are more prone to display compulsive
buying behaviour (e.g., Dittmar, 2005; Adamczyk et al., 2020).
Furthermore, sparse evidence suggests that smartphones may increase
online purchases (e.g., Eriksson et al., 2017; Bozaci, 2020). Thus, given
the smartphone-dependent and highly stimulating environment in
which Gen Z live and their higher likelihood of being compulsive buyers,
it is plausible that smartphones may promote the enactment of other
compulsive behaviours (Eide et al., 2018; Grifths et al., 2016). Overall,
there is a need to understand better the underlying mobile-related
mechanism that may inuence dysfunctional purchasing behaviours (i.
e., compulsive buying), especially among Gen Z individuals.
M.C. Mason et al.
Computers in Human Behavior 136 (2022) 107404
3
2.2. Smartphones in an extended S–O-R model-based conceptualization
The S–O-R model suggests that contextual stimuli (S) may inuence
an individual’s cognitive and affective states (i.e., related to their or-
ganism, O), triggering a behavioural response (R) (Mehrabian & Russell,
1974). In recent years, several investigations have extended the S–O-R
model to the context of information technology to deepen our under-
standing of human-machine interactions (Gatautis et al., 2016; Luqman
et al., 2017; Sohaib & Kang, 2015). Furthermore, the S–O-R model has
been applied heavily in studies focusing on the use of mobile phones
(Chen & Yao, 2018; Chopdar & Balakrishnan, 2020; Fang et al., 2017;
Hew et al., 2018; Zhang et al., 2020), and online mobile-related
behaviour (Chan et al., 2017). Within this theoretical framework, a
smartphone’s characteristics represent not just a stimulus but a ‘package
of stimuli’ (Luqman et al., 2017). The stimuli extracted from the virtual
environment trigger the formation of affective and cognitive states in the
consumers and ultimately lead to a specic behavioural response. Thus,
the current research posits that those over-attached to their devices (i.e.,
affected by smartphone addiction) will receive a higher number of
stimulative inputs.
In accordance with the S–O-R model, the internal cognitive state of
the users (O) are mood regulatory behaviours and ow experiences.
Moods are dened as “feelings that tend to be less intense than emotions
and that often (though not always) lack a contextual stimulus” (Hume,
2012, p. 260). Moods and emotions diverge in intensity and duration as
the former last longer and are less intense (Larsen, 2000). Furthermore,
while emotions are reactions to external events, moods are responses to
the state of the personal inner self (Larsen, 2000; Morris, 1992).
Consequently, mood regulation consists of all those actions and behav-
iours aimed at altering the subjective state (Larsen, 2000). The other
cognitive state which is triggered in smartphone over-attached users is
ow. Flow experiences have been dened as a cognitive state of total
absorption in an activity characterised by pleasant feelings and a loss of
sense of time (Csikszentmihalyi, 1975). In human-machine interactions,
this ow state is characterised by a total concentration on the activity
and a sense of enjoyment (Ghani & Deshpande, 1994).
Previous studies generally agree that a ow state is something that
most individuals have experienced in their lives through sports, board
games, dancing, reading, watching television, using smartphones, and
shopping (Gao & Bai, 2014; Liu et al., 2016; Nanda & Banerjee, 2020).
Flow plays a signicant role in understanding individuals’ online
behaviour (Islam et al., 2021). Moreover, ow experiences facilitate
exploratory consumer behaviours, causing, for example, an increase in
the time spent online. While most studies tend to overlook the down-
sides related to ow experience, some scholars have recently started to
relate ow experience and mobile phone addiction, advocating for a
possible connection between the two (Chen et al., 2017; Lee & Shin,
2016; Wang et al., 2020).
Besides being the primary vehicle for social interaction, smartphones
are among the most preferred platforms for young individuals for
shopping (Ameen & Anand, 2020; Bernstein, 2015). Furthermore, mo-
bile shopping offers consumers the possibility to purchase products in a
cashless manner while remaining hidden from the scrutiny of others,
features that may trigger the occurrence of compulsive buying behav-
iours (Dittmar et al., 2007). Thus, smartphones may also be means
through which compulsive buyers satisfy those uncontrollable urges that
characterise their pre-purchase phase.
Compulsive buying is “an uncontrollable drive or desire to obtain,
use, or experience a feeling, substance, or activity that leads an indi-
vidual to repetitively engage in a behaviour that will ultimately cause
harm to the individual” (O’Guinn & Faber, 1989, p. 148). Compulsive
buying is an addiction associated with guilt, harm, and a repetitive and
irresistible urge to purchase goods (Piquet-Pessˆ
oa et al., 2014) that are
often inexpensive and useless (Lejoyeux & Weinstein, 2010) and may
also emerge online (Duroy et al., 2014). Compulsive buying has been
traditionally related to several environmental and psychological factors
(Faber & O’Guinn, 2008; Frost et al., 2002; Valence et al., 1988).
However, most research on compulsive buying focuses on physical or
general online settings. On the contrary, the current study examined
online compulsive buying in relation to smartphone addiction. M-com-
merce is steadily growing, and the mobile environment is characterised
by other factors that may prompt compulsive behaviours in users. For
instance, overspending tendencies may be fuelled by the easy avail-
ability of products, engaging mobile shopping platforms, and the ease
with which compulsive buyers can buy something to regulate their
negative mood states. Noteworthy, the source of online compulsive
buying may not be the smartphone itself. Instead, it may be the conse-
quence of exposure to the mobile digital environment. Thus, it is
essential to focus on the potential relation between digital stimuli and
cognitive response to understand smartphone addictive behaviour and
its relationship with online compulsive buying.
In sum, the current research conceives smartphones as an appealing
‘package of stimuli’ capable of triggering specic internal processes (i.e.,
O; mood regulation and ow experience) which lead to behavioural
reactions (i.e., R; online compulsive buying). Fig. 1 depicts the proposed
in this research.
3. Conceptual model and hypotheses development
3.1. Mood regulation, smartphones and online compulsive buying
Several studies have suggested that dysfunctional and repeated
technology use may be related to the attempts to cope with negative
feelings (Caplan, 2010; Caplan et al., 2009; LaRose et al., 2003), as
“technology offers thrill and relief, and results in mood change” (Turel
et al., 2011, p. 1044). Individuals may develop problematic internet use
habits due to their attempts to use the net to reduce their negative
feelings such as loneliness, anxiety, stress, and depression (LaRose et al.,
2003). Besides interpersonal contact, smartphones offer instantaneous
access to several digital contents, from information and entertainment to
shopping platforms. Several authors have highlighted the link between
mood regulation and mobile devices (Chen et al., 2017; Chen et al.,
2019; Fu et al., 2020). Mobile virtual worlds may offer individuals
digital spaces to regulate their unpleasant moods and deal with negative
emotions (Yen et al., 2009). Hence, smartphone addiction may also be
seen as an abuse of a technological device to achieve mood modica-
tions. In line with this, the following hypothesis is proposed:
H1: Smartphone addiction is positively related to mood regulation
through smartphone use.
A potential relation between moods, mood regulation strategies, and
the development of pathological behaviours has been hypothesised by
several authors (e.g., Müller et al., 2012). For example, in their attempts
to escape from unpleasant moods, individuals may repeat pleasurable
activities, and this, in the long run, may result in the development of
behavioural addictions (Billieux, 2012). Furthermore, this mechanism
may be self-reinforcing: for example, pathological gamblers, kleptoma-
niacs, and compulsive buyers express “a decrease in these positive mood
effects with repeated behaviours or a need to increase the intensity of
behaviour to achieve the same mood effect, analogous to tolerance”
(Grant et al., 2010, p. 234). Compulsive buyers perceive increasing
anxiety levels that lower only when the purchase has been made (Black,
2007). In other words, the act of buying in compulsive buyers is likely a
response to a negative mood state (Dittmar et al., 2007; Donnelly et al.,
2013; Faber & Christenson, 1996). Compulsive buyers experience a
short-term improvement in their mood through the purchase, which
works as a positive reinforcement mechanism. This suggests that, like
other compulsive behaviours, mood regulation, and even the enjoyment
experienced by the customer while buying, can be involved in
compulsive buying (Christenson et al., 1994; Faber et al., 1987; Mil-
tenberger et al., 2003). In line with this, the following hypothesis is
proposed:
H2: Mood regulation is positively related to online compulsive
M.C. Mason et al.
Computers in Human Behavior 136 (2022) 107404
4
buying.
There are several similarities between compulsive buying and
smartphone addiction. Both have been regarded as attempts to escape
from internal negative feelings and focus on external stimuli. Thus, it is
arguable that, for some users, dysfunctional and mobile-based mood
regulation strategies may act as an enforcing factor of the relationship
between online compulsive buying and smartphone addiction. Thus,
people may select specic content (e.g., shopping items) to regulate their
affective states, reduce negative moods, and achieve optimum arousal
(Hoffner & Lee, 2015). In line with this, the following hypotheses are
proposed:
H2a: Mood regulation through smartphone use acts as a strength-
ening mediator of the relationship between smartphone addiction and
online compulsive buying.
Mood regulation behaviours may be dened as actions taken by in-
dividuals to reduce dysphoric moods (Turel et al., 2011). Such behav-
iours usually comprise an active escape from real-life problems and may
also be vehiculated through digital devices. Moods are less intense than
emotions, and as they “tend not to disrupt ongoing activity” (Kraiger
et al., 1989, p. 13), prior research has associated mood regulatory
mechanisms to the experience of ow states. For example, Hu et al.
(2019) and Zhang et al. (2014) posited that a mood mechanism may be
involved in the experience of ow while using digital devices. Through
mood regulatory actions, individuals get involved in pleasant activities,
prompting the achievement ow states (Li & Browne, 2006). Thus, the
following hypothesis is proposed:
H3: Mood regulation through smartphone use is positively related to
ow experiences triggered by smartphone use.
3.2 Flow experiences, smartphones, and online compulsive buying
Several scholars have investigated the relationship between ow
states and smartphone use (Ameen et al., 2020b; Chou & Ting, 2003;
Leung, 2020; Wang et al., 2020). In general, the literature suggests that
smartphones undermine individuals’ ability to achieve a ow state, as
some mobile-related behaviours – such as continuous phone checking –
may interrupt the states of total concentration needed to experience ow
(Duke & Montag, 2017a). Still, scholars do not exclude the possibility
that individuals may experience ow through mobile devices (Duke &
Montag, 2017b). In line with this latter point, Leung (2020) reported
that, while engaging in hedonic (e.g., playing video games, watching
videos, or online shopping) and eudemonic activities (e.g., socialising,
reading the news, and internet surng), smartphone users are likely to
achieve a state of ow. Furthermore, the massive variety of functions
and applications present on smartphones may easily arouse one interest
and, as the immersion and the attraction toward an activity rise, in-
dividuals will dwell into them and ignore everything else, thus experi-
encing ow states (Wang et al., 2020). Moreover, Khang et al. (2013)
reported that the amount of time spent on the device was signicantly
related to higher chances of experiencing ow states for mobile users. To
synthesise, for smartphone-addicted users – those who spent the most
time looking at their mobiles - there may be an increased likelihood of
reaching ow states. Thus, the following hypothesis is proposed:
H4: Smartphone addiction is positively related to ow experiences
triggered by smartphone use.
Flow experience has been recently addressed as an inuent deter-
minant of users’ online behaviour. Ettis (2017) highlighted a positive
relationship between ow experiences and consumers’ purchase and
revisit intentions in online shopping websites. Similar results were re-
ported by Kim and Han (2014), Kim et al. (2017) and Zhou et al. (2010).
Higher ow levels are associated with a higher number of purchases,
satisfaction, loyalty and longer hours spent on the internet (Herrando
et al., 2019; Lee et al., 2019; Niu & Chang, 2014). More than this, when
consumers are in ow states, their decisions are less well thought out
(Barta et al., 2021). This can ease the senseless and item-unrelated
purchase of products typical of compulsive buyers. In addition, in on-
line environments, consumers have complete freedom to browse
around, and staff and other consumers are absent, both factors which
may enhance compulsive buying tendencies (Dittmar et al., 2007). In
line with this, the following hypothesis is made:
H5: Flow experience through smartphone use is positively related to
online compulsive buying.
Considering the role of ow in relation to the amount of time users
spend on web platforms, researchers have advocated for implementing
in-app gamication strategies to encourage further in-app engagement
and prompt ow experiences (e.g., Dhir et al., 2020; Ozkara et al., 2017).
Hence, mobile digital environments are nowadays designed to facilitate
ow experiences (Ali, 2016) to capture users’ attention and boost their
purchase intentions. This approach can be seen in mobile retail apps, but
the potential consequences on consumers’ attitudes and behaviours
have remained underexplored. Moreover, ow experiences are associ-
ated with positive feelings that people are usually eager to experience
again and, as reported in Niu and Chang (2014), generate positive ef-
fects in terms of consumers’ buying behaviour strengthening such
behaviour. Some evidence suggests that ow is signicantly correlated
with compulsive buying tendencies (Horv´
ath & Adıgüzel, 2018). Thus, it
Fig. 1. The proposed theoretical model.
M.C. Mason et al.
Computers in Human Behavior 136 (2022) 107404
5
may be hypothesised that a ow state achieved through a smartphone
may indeed prompt online compulsive buying. Coherently with the
literature above examined, the following hypotheses are proposed:
H5a: Flow experience through smartphone use acts as a strength-
ening mediator of the relationship between smartphone addiction and
online compulsive buying.
3.3. The relationship between smartphone addiction, ow experience,
mood regulation, and online compulsive buying
Smartphones are a fundamental tool for most people, especially
young individuals (Brito et al., 2021). These are used to maintain social
relationships, organise work and study days, for mood-regulatory pur-
poses (Chen et al., 2017), and purchase goods. About the latter, mobile
shopping is different from brick-and-mortar shopping. Like the other
forms of online shopping, it allows compulsive buyers to avoid the social
stigma connected with compulsive buying, and more than this, all online
transactions are de facto cashless transactions (Dittmar et al., 2007;
Kukar-Kinney et al., 2009). Thus, mobile shopping conveys all these
tempting traits searched by compulsive shoppers who do not want
others to know their obsession with shopping and what to buy in an
unregulated manner. Previous literature on smartphone addiction stated
that the higher the addiction, the higher the smartphone usage fre-
quency (Konok et al., 2016). Chopdar et al. (2022) found a strong and
positive effect of smartphone addiction on the frequency of shopping on
mobile app platforms. Thus, mobile addicted users are more prone to
shop more frequently over m-shopping applications. Given the advan-
tages of m-shopping for compulsive buyers and the relationship between
m-shopping and smartphone addiction, it may be claimed that smart-
phones may foster compulsive buying tendencies of some individuals
while shopping and buying online through their mobiles. Furthermore,
Choi et al. (2019) pointed out that several online maladaptive behav-
iours (e.g., compulsive gaming, social media abuse, and shopping) may
be driven by constant accessibility via smartphones and other mobile
device technologies. Thus, it supports the hypothesis of a positive rela-
tion between smartphone addiction and online compulsive buying.
H6: Smartphone addiction is positively related to online compulsive
buying.
Furthermore, the medium (i.e., smartphone) itself may not be
addictive, but it is used to access to platform/source that promotes ad-
dictions (Grifths et al., 2016). Researchers have indicated that
emotional buying, mood (i.e., retail therapy) and experiencing ow
affect online buying (Dittmar et al., 2007; Niu & Chang, 2014). Mobiles
allow access to many social media applications. Brands and products are
displayed on these platforms, and users’ behaviours are tracked. Re-
tailers and companies are facilitated in delivering ad-hoc advertising
content, through which consumers may be directly redirected to shop-
ping platforms. These platforms are designed to show only relevant
content and be stimulating and ow-inducing, encouraging and
fostering user engagement (Choi et al., 2008; Kang et al., 2015; Xu et al.,
2015). While this may be appealing for online sellers, it is plausible that
negative consequences are present for some individuals, and these lie
unexplored to the best of the authors’ knowledge. Compulsive buyers,
who buy online also regulate their mood, may be captured by the tril-
ling, tempting, ow-inducting characteristic of the mobile shopping
platform and, consequently, dwell more easily into dramatic purchasing
sprees, as they are characterised by a reduced capacity for self-control
and lower resistance to external triggers (Maccarrone-Eaglen & Scho-
eld, 2017Maccarrone-Eaglen & Schoeld, 2017). Thus, the interaction
between ow experience and mood regulation triggered by smartphone
use likely enhances the possibility of buying compulsively online.
Hence, ow experience and mood regulation are important factors in
studying the relationship between smartphone addiction and online
compulsive buying tendencies. Therefore, the following hypothesis is
proposed:
H6a: Flow experience and mood regulation triggered by smartphone
use act as strengthening mediators of the relationship between smart-
phone addiction and online compulsive buying.
4. Method
The data collection for the current research was performed through a
survey carried out in Italy (See Table 2). The survey target population
included students between 18 and 24 years enrolled in high schools or
universities. The participants were recruited in Italy, a country where
the number of smartphones is higher than that of the inhabitants: about
80 million mobile devices for a population of 60 million. In Italy, 86% of
individuals between 18 and 24 own and actively use a smartphone for
chatting, playing, and shopping (Censis, 2019). The survey was publi-
cised to high-school and universities; thus, every student had an equal
opportunity to participate in the survey. Using a systematic sampling
approach, 300 participants were randomly selected among those who
agreed to participate. Incomplete responses were eliminated, leaving a
useable sample of 275 completed questionnaires (131 secondary school
students and 144 university students). Therefore, the nal sample was
formed by 275 participants with a prevalence of females (65%) with a
mean age of 20 (SD =2.50). The main characteristics of this sample are
presented in Table 1. All participants signed a written informed consent
before taking part in the study. The local review board approved the
study of the University of Udine (Italy).
The 20-min survey was conducted face-to-face between February
2018 and January 2019. The questionnaire was developed after a
thorough literature review and consisted of two parts. The former
investigated the socio-demographic prole of the respondents. The
latter consisted of four scales: (1) online compulsive buying (2)
smartphone-induced ow experience (SFE) (3) smartphone addiction;
and (4) mood regulation while using smartphones (MRS) with response
options on a seventh-point Likert scale format ranging from 1 (abso-
lutely disagree) to 7 (absolutely agree). The online compulsive buying
scale was adapted from Edwards (1993) and Valence et al. (1988) and
slightly modied to t online purchases. The SFE scale was adopted
from Ghani and Deshpande (1994). The items forming the smartphone
addiction and MRS scales were derived from Caplan (2010) and
Olivencia-Carri´
on et al. (2018) and adapted to the case of smartphone
addiction. Since the data collection was carried out in Italy, the items
were translated from English into Italian with a double translation
method. The nal English version of the questionnaire was translated
into Italian by a professional bilingual translator, uent in both English
and Italian. Before the formal survey, ten Gen Z respondents (excluded
from the main study) were randomly selected for pre-testing. The Italian
version of the questionnaire was given to them to test items’ clarity and,
accordingly, several corrections to the terminology were made to reduce
the ambiguity and to avoid content redundancy.
4.1. Construct validity and reliability
A two-stage approach was used to assess factorial validity: an
Table 1
Demographic prole of the interviewed.
Variable Freq. % Cum. %
Gender
Male 96 34.90
34.90
Female 179 65.10 100.00
Education Level
Middle School 105 38,20 38.20
Secondary School 145 52.70 90.90
Bachelor’s degree 25 9.10 100.00
Age
18–20 155 56.40 56.40
21–23 72 26.10 82.50
>23 48 17.50 100.00
M.C. Mason et al.
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6
exploratory factor analysis (EFA) was followed by conrmatory factor
analysis (CFA). An EFA with maximum likelihood and a varimax rota-
tion with Kaiser normalisation was performed to dene the latent di-
mensions that underlie the data. The latent root criterion was used for
factor inclusion (i.e., eigenvalue equal to or higher than 1, and a factor
loading of 0.4 was used as a cut-off value to include the items in each
factor). The appropriateness of factor analysis was conrmed by the
Kaiser–Meyer–Olkin measure of sampling adequacy (KMO =0.896) and
Bartlett’s test of sphericity (p-value <0.001). The rotation converged in
ve iterations. The four extracted factors, namely smartphone addiction,
MRS, online compulsive buying, and SFE, accounted for 71% of the total
variance. The internal consistency of the measures was assessed by
computing the Cronbach’s alpha for all the extracted factors, showing
for all the factors values higher than 0.80. Then, using the maximum
likelihood method, a CFA was conducted to establish condence in the
measurement model. Most of the goodness-of-t indexes were higher
than the thresholds indicated in the literature (
χ
2 =464.953, df =183,
χ
2/df =2.541, RMSEA =0.075, CFI =0.93, TLI =0.920, NFI =0.890,
SRMR =0,050) (Hair et al., 2019)., while the NFI was the only lower
than 0.90 indicated in Bentler and Bonnet (1980). Nevertheless, as all
the other indexes aligned with the commonly used cut-offs, the mea-
surement model was still deemed to have an adequate t for the data.
Each construct displays an AVE higher than 0.50 and composite
reliability higher than 0.7 (Table 3). To analyse discriminant validity,
the AVEs related to each latent construct were compared to the squared
correlations between the corresponding constructs: the discriminant
validity condition was satised as all the AVEs for the latent constructs
exceeded the respective squared correlations (Fornell & Larcker, 1981).
Last, all the items showed to load positively and signicantly onto the
respective constructs (values ranging from 0.503 to 0.943).
4.2. Common method Bias
To conrm that common method bias (CMB) did not affect the data,
Hartman’s single-factor test was conducted using SPSS 23. The single
factor did not account for most of the variance (34.54%), hinting at the
absence of CMB. The so-called marker variable technique (Lindell &
Whitney, 2001) was also employed to gain additional condence. A
marker variable is theoretically unrelated to the substantive study var-
iables of interest, and this was represented by the construct of “Bargai-
ning Proneness” adapted from Harris and Mowen (2001). The
standardised factor loadings on the marker for the items associated with
the investigated constructs ranged from −0.05 to 0.131. None of them
loaded at p <0.01, and, at the same signicance level, the marker
Table 2
CFA with factor loadings, AVE, CR and Cronbach’s
α
.
Construct Measurement items Factor
Loading
T-
Value
CR AVE
α
Online Compulsive Buying Adapted from Edwards (1993) and Valence et al. (1988) 0.93 0.59 0.92
As soon as I enter in an online shopping platform, I have an irresistible urge to go into a
shop to buy something.
0.79 15.53
I often have an unexplainable urge, a sudden and spontaneous desire, to go and buy
something online.
0.79 15.38
I am often impulsive in my online buying behaviour 0.76 14.67
For me, shopping is a way of facing the stress of my daily life and of relaxing. 0.73 13.92
I sometimes feel that something inside pushes me to buy online. 0.69 12.92
I buy things online I don’t need or won’t use 0.82 16.34
I go on online buying binges. 0.82 16.36
I feel “high” when I go on a buying spree 0.76 14.79
I feel driven to shop and spend online, even when I don’t have the time or the money. 0.75 14.30
Smartphone addiction Adapted from Caplan, 2010 and Olivencia-Carri´
on et al. (2018) 0.82 0.57 0.84
When I am not using the smartphone, I have a hard time trying to resist the urge to use
it.
0.81 15.29
I have difculty controlling the amount of time I spend on the smartphone. 0.75 13.85
I nd it difcult to control my smartphone use. 0.75 13.66
I have tried to spend less time on my smartphone, but I am not able to do it. 0.71 12.86
Mood Regulation through
Smartphone
Adapted from Caplan (2010) 0.92 0.78 0.93
I have used the smartphone to make myself feel better when I’ve felt upset. 0.90 19.17
I have used the smartphone to make myself feel better when I was down. 0.88 18.29
I have used the smartphone to forget about my problems 0.90 19.11
I have used the smartphone to forget about my worries 0.86 17.73
Smartphone-induced Flow
Experience
Adapted from Ghani & Deshpande, 1994 0.86 0.64 0.87
When I use the smartphone, I am absorbed intensely by it. 0.94 20.69
When I use the smartphone, I am deeply engrossed by it. 0.85 17.73
When I use the smartphone, I am fully concentrated on it. 0.85 17.17
Using my smartphone is enjoyable. 0.50 8.63
Note 1: Fit indexes
χ
2
(183) =464.953,
χ
2
/df =2.541, RMSEA =0.075, CFI =0.93, TLI =0.920, NFI =0.890, SRMR =0,050.
Table 3
Correlations among constructs and square root of AVE.
Construct M SD Gender Age Marker OCB SA MRS SFE
Gender - - -
Age 20.50 2.60 0.08 –
Marker 3.07 1.69 0.27 −0.23 –
OCB 1.85 1.11 −0.11 −0.09 0.14 0.76
SA 2.37 1.29 −0.16 −0.07 0.03 0.28 0.75
MRS 3.44 1.76 −0.29 −0.21 −0.00 0.22 0.48 0.88
SFE 3.77 1.39 −0.22 0.02 −0.04 0.36 0.50 0.47 0.80
Note 1: OCB: online compulsive buying, SA: smartphone addiction, MRS: mood regulation while using smartphones, SFE: smartphone-induced ow experience.
Note 2: The marker variable was “Bargaining Proneness” adapted from Harris and Mowen (2001).
Note 3: Square root of AVE on diagonal in bold. Correlations among factors under the diagonal.
M.C. Mason et al.
Computers in Human Behavior 136 (2022) 107404
7
variable was unrelated to all other constructs (correlation ranged from
−0.006 to 0.141). Furthermore, all the signicant correlations among
the other constructs remained signicant after introducing the marker
variable. Overall, the results of these tests indicated that the distortion
imputable to a method bias is marginal.
4.3. Analysis method
The proposed theoretical model posited that (1) smartphone addic-
tion and online compulsive buying are positively related; and (2) MRS
and SFE would mediate the effect of smartphone addiction on online
compulsive buying. In the model, gender and age were added as cova-
riates. To explore these predictions, the proposed theoretical model was
tested with a serial mediation analysis with two mediators using the
Process macro (Hayes, 2017) for SPSS (Model 6, 5.000 bootstraps; 95%
bias-corrected condence intervals) to evaluate the following serial
mediation: smartphone addiction → MRS → SFE → online compulsive
buying.
Further to the serial mediation analysis, asymmetrical modelling
using fsQCA was employed to investigate the data further and observe
which congurations of antecedents (i.e., smartphone addiction, SFE,
MRS, age and gender) are likely to investigate lead to high scores in
online compulsive buying. Unlike the methodologies grounded on
probability theory, qualitative comparative analysis fully embraces
complexity theory and, relying on Boolean algebra, aims to explain
whether conditions (or congurations of causal conditions) are neces-
sary or sufcient for a specic outcome to occur (Ragin, 2009). As far as
consumers’ behaviour studies are concerned, several authors (Pappas
et al., 2016; Pappas et al., 2020; Schmitt et al., 2017; Urue˜
na & Hidalgo,
2016) have adopted this methodological approach to examine how
relevant antecedents combine to lead to a behavioural outcome.
5. Results
5.1. Serial mediation model
The results from the serial mediation analysis showed that the in-
direct pathway from smartphone addiction to online compulsive buying
through MRS and SFE was signicant since the condence interval did
not include the zero (Indirect 3: indirect effect =0.031, 95% C.I.:0.013
to 0.054), thereby supporting the hypothesis that MRS and SFE play a
mediating role in the relationship between smartphone addiction and
online compulsive buying. Hence, H6a was supported by the model. As
for the individual indirect pathways, the one involving only MRS was
found not signicant (Indirect 1: indirect effect =0.042, 95% CI: −0.012
to 0.101), while the one involving only SFE was signicant (Indirect 2:
indirect effect =0.081, 95% C.I.:0.036 to 0.132). These results sup-
ported the hypothesis that SFE mediates the relationship between
smartphone addiction and online compulsive buying while rejecting the
one involving MRS. Thus, H2a was rejected, and H5a was accepted.
Looking at the direct paths, the analyses showed that smartphone
addiction has a signicant and positive relationship with MRS (beta =
0,538, SE =0.071, p.<0.001), supporting H1 and suggesting that people
may tend to self-regulate their mood by using the smartphone. The
model also supported the relationships between smartphone addiction
and SFE (beta =0.388; SE =0.059; p.<0.001) and between MRS and
SFE (beta =0.247, SE =0.059, p.<0.001), conrming H4 and H3,
respectively. Surprisingly, the model did not conrm the relationship
between MRS and online compulsive buying (beta =0.063, SE =0.040,
p. =0.118) therefore rejecting H2. H5 was supported as the relationship
between SFE and online compulsive buying was positive and signicant
(beta =0.191, SE =0.050, p.<0.001). Finally, even if the model showed
the direct pathway was not signicant (beta =0.083, SE =0.052, p. =
0.115), the total effect model showed that the relationship between
smartphone addiction and online compulsive buying was signicant
(beta =0.207, SE =0.047, p.<0.001), leading to the partial acceptance
of H6 and suggesting the presence of indirect-only mediation effect
(Zhao et al., 2010).
Upon examining the effects of the control variables (i.e., gender and
age), the results suggested that dysfunctional use of smartphones to
mood regulate is more prominent in younger participants (beta =
−0.541, SE =0.185, p.<0.001) and females (beta = − 0.820, SE =0.185,
p.<0.001) and that being female may also facilitate experiencing a ow
state while using the smartphone (beta = − 0.335, SE =0.152, p.<0.05).
No signicant effects of the covariates on compulsive buying were
found. Detailed results of the model are shown in Table 4, while the
estimated model is displayed in Fig. 2.
5.2. Fuzzy QCA methodology and calibration
After serial mediation analysis, asymmetrical modelling was used
and, using fsQCA, the congurations of antecedents (i.e., smartphone
addiction, MRS, SFE, age, gender) that can explain high scores in online
compulsive buying were investigated. First, to perform the FsQCA’s al-
gorithm, all the variables must be calibrated into fuzzy sets, with values
ranging from 0 to 1. In the present study, factors were directly calibrated
(Ragin, 2009) using three anchors: full-membership, cross-over point,
and non-membership. Calibration is a half empirical and half theoretical
process (Greckhamer et al., 2018). Hence it should nd its basis in
previous theoretical knowledge. Although when calibration criteria are
not available from previous research on the same topic, empirical cali-
bration is recommended, and the data are calibrated using percentile
distributions of the variables. Thus, for smartphone addiction, MRS and
SFE, the full-membership threshold was set at the 90th percentile, the
non-membership method threshold was set at the 10th percentile, and
the cross-over point was set at the median value of each. Regarding
gender, the value 1 was set for males and the value to 0 for females.
Finally, age and online compulsive buying were calibrated based on
previous theoretical knowledge. For age, the full-membership anchor
was set at 18, the cross-over anchor was set at 20, and the
non-membership anchor was set at 25 (Dittmar, 2005). Regarding online
compulsive buying, the criteria suggested in O’Guinn and Faber (1989)
and Faber and O’Guinn (1992) were followed. Hence, the
full-membership point was set two standard deviations from the mean
value of the measure (Adamczyk et al., 2020; Huang & Chen, 2017), the
cross-over point was set at the median value, and the non-membership
point at the 10th percentile. Table 5 summarises the calibration pro-
cess showing the thresholds used and the descriptive statistics of the
calibrated causal conditions and outcomes.
5.3. Fuzzy set QCA results
Following the calibration, analyses of necessity and sufciency were
performed. Since fsQCA enables asymmetrical causal relationships,
what explains the outcome’s presence may be different from what ex-
plains its absence. Thus, both the presence and absence of the outcome
were tested.
The rst analysis conducted using fsQCA was the analysis of neces-
sity. Such analysis is used to evaluate whether the causal conditions are
necessary for a given outcome to occur. Setting as outcomes both high
and low online compulsive buying (i.e., ~ online compulsive buying; in
FsQCA, “~” stands for the negation of the outcome or a causal ante-
cedent), the consistency scores for either presence or absence of every
single congurational antecedent ranged between 0.327 and 0.710, not
surpassing the threshold for necessity which is usually set at 0.80 (Ragin,
2000). Hence, none of them can be considered necessary for explaining
high and low values in online compulsive buying. Details of the analysis
of necessity can be found in Table 5.
Since none of the causal conditions can be considered as a necessity
for the outcome of interest, the sufciency analysis was performed.
Sufciency analysis identies the different combinations of causal
conditions that meet specic criteria of sufciency for the outcome to
M.C. Mason et al.
Computers in Human Behavior 136 (2022) 107404
8
Table 4
Result of the model.
Outcome variables
To MRS To SFE To OCB
Coeff. SE p Coeff. SE p Coeff. SE p
Covariates
Gender −0.820 0.194 >0.001 −0.335 0.152 >0.05 0.012 0.128 0.992
Age −0.541 0.185 >0.01 0.203 0.143 0.156 −0.150 0.119 0.209
Antecedents
Constant 2.689 0.232 >0.01 2.142 0.215 >0.001 0.737 0.209 >0.001
SA 0.538 0.071 >0.001 0.338 0.059 >0.001 0.083 0.052 0.115
MRS – – – 0.247 0.046 >0.001 0.063 0.040 0.118
SFE – – – – – – 0.191 0.050 >0.001
R
2
=.269 R
2
=.321 R
2
=.158
F(3,271) =32.70
p.<0.001
F(4,270) =31.30
p.<0.001
F(5,269) =10.15
p.<0.001
Total Effect Model Indirect Effects
To OCB Path Effect SE LLCI ULCI
Coeff. SE p Total Indirect 0.155 0.037 0.081 0.229
Covariates Indirect 1
Gender −0.153 0.127 0.228 SA→MRS→OCB 0.042 0.028 −0.012 0.101
Age −0.171 0.121 0.160 Indirect 2
Antecedents SA→SFE→OCB 0.081 0.024 0.036 0.132
Constant 1.444 0.152 >0.001 Indirect 3
SA 0.207 0.047 >0.001 SA→MRS→SFE→OCB 0.031 0.010 0.013 0.054
R
2
=.088
F(3,271) =8.73
p.<0.001
Note 1: OCB: online compulsive buying, SA: smartphone addiction, MRS: mood regulation while using smartphones, SFE: smartphone-induced ow experience.
Note 2: Gender was coded one for male and zero for female.
Fig. 2. The estimated model.
Table 5
Calibration thresholds, descriptive statistic of the causal conditions and analysis for necessity.
Fuzzy Set Measures Calibration thresholds Descriptive Statistics Analysis of Necessity
Full-membership Cross-over point Non-membership Mean SD Consistency Coverage
OCB 3.80 1.40 1.00 .440 .330 – –
SA 4.25 2.00 1.00 .488 .355 .710(.549) .640(.473)
MRS 6.00 3.50 1.00 .482 .345 .682(.574) .622(.488)
SFE 5.50 3.75 2.00 .491 .346 .703(.543) .630(.470)
Age 18 20 25 .556 .370 .701(.506) .506(.503)
Male 1 0 .349 .476 .327(.672) .412(.455)
Note 1: OCB: online compulsive buying, SA: smartphone addiction, MRS: mood regulation while using smartphones, SFE: smartphone-induced ow experience.
Note 2: Gender was coded one for male and zero for female.
Note 3: In the analysis for necessity, consistency and coverage scores between parenthesis are the ones for the negation of the causal antecedents.
M.C. Mason et al.
Computers in Human Behavior 136 (2022) 107404
9
occur. Thus, the fuzzy-set algorithm was used to determine the combi-
nations likely to lead to high levels and low levels of online compulsive
buying (i.e., ~ online compulsive buying). FsQCA’s algorithm produced
a true table of 2
k
rows for both analyses, where k is the number of causal
combinations considered (i.e., ve). Then, each true table was rened
considering two cut-off values: 0.80 for consistency scores and 3 for the
observations’ frequency. Both values align with the commonly recom-
mended ones (Ragin, 2009). In Table 6, the results of the fuzzy set
analysis are displayed. In this, the crossed circles (⊗) indicate the
absence of a causal condition, while the black circles (●) signal the
presence of a condition. Large circles identify core elements, while small
ones represent peripheral elements. The blank spaces stand for the “do
not care” situation and are related to the unimportance of the presence
or absence of a causal condition (Fiss, 2011).
Inspecting the results obtained for high values in online compulsive
buying, the overall solution consistency score of 0.83 indicated a robust
relationship between the outcome and the combination of recipes.
Furthermore, the overall solution coverage was 0.34. Solution coverage
suggests the extent to which the output can be determined based on the
proposed congurations and can be compared to the R
2
value for sym-
metrical methods (Woodside, 2013). Hence, the three solutions pro-
posed accounted for a substantial proportion of the outcome. Looking at
the consistency scores for the three congurations, these were higher
than 0.84. Thus, they all presented acceptable degrees of approximation
(Ragin, 2009). Additionally, fsQCA software also estimates raw and
unique coverage for each conguration, which represents their empir-
ical relevance. Raw coverage is the amount of the outcome explained by
a solution, while unique coverage is the amount exclusively explained
by a conguration. All the combinations showed unique coverages
higher than 0. Hence, all these solutions were considered empirically
relevant. Similarly, for low values in online compulsive buying, both
overall solution consistency (0.86) and coverage were acceptable (0.39).
Furthermore, in this case, the consistency and coverage of the individual
congurations were in line with the recommended thresholds (Ragin,
2009).
In the analysis of the congurations for the presence of online
compulsive buying, the solutions’ table indicated that a different pattern
of core, peripheral, and neutral conditions existed for each congura-
tion. Specically, in conguration C1, smartphone addiction, MRS and
being male were identied as core conditions for online compulsive
buying, while being around 25 years old (i.e., ~ age) represented a
peripheral condition. In conguration C2, smartphone addiction and
being around 18 years old were addressed as core conditions, while ~
MRS was regarded as peripheral. Similarly, C3 shared the same core
conditions as S2, although, this time, the peripheral condition was
represented by being male. Most notably, smartphone addiction was
identied as a core condition for all the proposed causal congurations.
Furthermore, S2 displayed the largest unique coverage (0.18), afrming
itself as the most empirically relevant solution.
Upon examining the congurations proposed for the absence of on-
line compulsive buying (i.e., ~ online compulsive buying), the solutions
table showed different patterns of core, peripheral, and neutral condi-
tions. In C1 and C5 congurations, smartphone addiction, ~ MRS and
~SFE were identied as core conditions for online compulsive buying.
Furthermore, in C1, ~ Age represented a peripheral condition, and
likewise did being male for C5. In C2 conguration, ~ smartphone
addiction, being male and around 18 years old, constituted the core
conditions, while ~ MRS was regarded as a peripheral condition. Lastly,
C3 and C4 shared the same core conditions, which were ~ smartphone
addiction and MRS. Looking at their peripheral conditions, for C3, they
were being male and ~SFE, while for C4 they were SFE and being male
around 25 years old (i.e., ~ age). All the congurations explaining ~
online compulsive buying presented a sort of alternance between the
presence and absence of smartphone addiction and MRS or SFE.
Simplifying, the solutions that presented the presence of smartphone
addiction as a core condition were characterised by the absence of MRS
or SFE as a core condition as well, and vice versa. Finally, fsQCA’s
ndings provided clear evidence of asymmetric causality and equin-
ality: different congurational sets were able to lead to high and low
online compulsive buying outcomes.
6. Discussion
This study aimed at investigating the relationship between smart-
phone addiction and online compulsive buying among Gen Z consumers
within the conceptual framework of the S–O-R model. In summary, this
research offers empirical evidence that a potential relationship between
smartphone addiction and online compulsive buying may be present and
that MRS and SFE may play a role in this relationship.
The serial mediation analyses showed that, after introducing MRS
and SFE as mediating variables, the direct effect of smartphone addic-
tion on online compulsive buying was not signicant anymore. This
suggests that smartphone addiction indirectly affects online compulsive
buying via MRS and SFE. The indirect-only mediation effect highlights,
on one side, the importance of organism components (i.e., ow and
mood regulation) and, on the other, that the compulsive behaviour
seems to be a response to an internal state rather than a direct reaction to
technology-induced stimuli. Thus, the sole over-exposition to the
smartphone environment may not necessarily lead to online compulsive
buying. Furthermore, the mediating effects of SFE and MRS affected the
relation between smartphone addiction and online compulsive buying in
different ways. Indeed, the isolated indirect effect of SFE was signicant,
while the isolated indirect effect of MRS was not. Therefore, MRS per se
did not mediate the relationship between smartphone addiction and
Table 6
Intermediate solutions for high score on the compulsive buying scale.
Congurations High OCB Low OCB
Solutions Solutions
C1 C2 C3 C1 C2 C3 C4 C5
SA ⚫ ⚫ ⚫ ⚫ ⨂ ⨂ ⨂ ⚫
MRS ⚫ ⨂ ⨂ ⨂ ⚫ ⚫ ⨂
SFE ⚫ ⚫ ⨂ ⨂ ● ⨂
Male ⚫ ● ⚫ ● ⨂ ●
Age ⨂ ⚫ ⚫ ⨂ ⚫ ⨂
Consistency 0.89 0.84 0.86 0.93 0.87 0.92 0.90 0.89
Raw Coverage 0.10 0.26 0.11 0.20 0.15 0.10 0.12 0.12
Unique Coverage 0.04 0.18 0.02 0.05 0.06 0.02 0.05 0.01
Solution coverage 0.34 0.39
Solution consistency 0.84 0.86
Note 1: OCB: online compulsive buying, SA: smartphone addiction, MRS: mood regulation while using smartphones, SFE: smartphone-induced ow experience.
Note 2: Consistency threshold: 0.80; observation threshold: 3.
M.C. Mason et al.
Computers in Human Behavior 136 (2022) 107404
10
online compulsive buying, while SFE did. This suggests that online
compulsive buying may be more related to the experience of a state of
ow rather than a direct response to a negative mood. Overall, the
symmetrical modelling results align with those reported by Horv´
ath and
Adıgüzel (2018) and Niu and Chang (2014), where ow experience was
positively related to consumers’ dysfunctional buying behaviour. Still,
concerning specically MRS, the results do not align with previous
investigation outcomes as these sustained that mood-regulating pro-
cesses strengthen compulsive buying (e.g., Dittmar et al., 2007; Donnelly
et al., 2013). Nevertheless, the total indirect effect assessing the serial
mediation between smartphone addiction and online compulsive buying
through MRS and SFE was signicant. This novel result suggests that
individuals with smartphone addiction may display a higher likelihood
of mood-regulating by using their mobiles, which may facilitate expe-
riencing ‘ow’ states and ultimately increase the likelihood of displaying
compulsive buying tendencies while online.
As for the fsQCA analysis, the solution table for achieving high levels
of online compulsive buying consists of three congurations. Firstly,
smartphone addiction was a core condition in all solutions. Hence, also
the asymmetrical modelling supports the hypothesis that a dysfunctional
use of mobiles may be related to the development of online compulsive
buying behaviours. Secondly, all three congurations showed a combi-
nation between smartphone addiction and MRS or SFE. Furthermore, in
the solution table for achieving low levels of online compulsive buying
(i.e., ~ online compulsive buying), all the congurations showed an
alternation between the presence or absence of smartphone addiction
and MRS or SFE. Both results suggest that the relationship between
online compulsive buying and smartphone addiction was also inu-
enced by the interaction with cognitive factors (i.e., MRS and SFE). Of
note, these two causal conditions are never co-present in explaining the
presence of online compulsive buying. This enriched symmetrical
modelling results by suggesting that, for a part of the sample, compul-
sive buying is more related to the experience of ow states, while, for a
minor portion of the sample, to mood regulation. Nevertheless,
regarding fsQCA, the result conrmed that both above-mentioned fac-
tors may be involved in online compulsive buying behaviours, as pre-
vious research suggested (Dittmar et al., 2007; Donnelly et al., 2013;
Horv´
ath & Adıgüzel, 2018). Lastly, age was addressed as a core condi-
tion in two congurations (i.e., C2 and C3 in Table 6). Considering that
C2 was the most empirically relevant solution (highest unique
coverage), and in line with previous research conducted by Dittmar
(2005), online compulsive buying may indeed be more prominent
among those younger Gen Z individuals that may still be considered as
adolescents (around 18 years old). Nonetheless, this was not supported
by the serial mediation model. Therefore, future studies should explore
this potential interaction between different ages and online compulsive
buying inside the Gen Z cohort.
The results from serial mediation and FsQCA analyses converge in
suggesting that smartphone addiction and online compulsive buying are
related and that MRS and SFE play a role in this relationship. To sum-
marise, dysfunctional smartphone users may use their devices to regu-
late their dysphoric moods, implying greater exposure to online
environments such as shopping platforms or social media and their
funny and thrilling characteristics. This may signicantly contribute to
the generation of ‘ow’ states (e.g., while browsing ow-inducing
shopping platforms), which may act as a trigger for their compulsive
buying behaviours.
6.1. Theoretical contributions
From a theoretical point of view, the current research contributes to
both the body of knowledge around compulsive buying and smartphone
addiction. First, it highlights a novel connection between online
compulsive buying and smartphone addiction in Gen Z. Previous
research on compulsive buying focused on the role of the online envi-
ronment (e.g., Dittmar et al., 2007; Kukar-Kinney et al., 2009; 2016).
However, the role that a specic device (i.e., smartphones) may have in
fostering such compulsive behaviours has not been considered. Simi-
larly, the literature concerning smartphone addiction never considered
its potential downsides and the development of compulsive buying
tendencies (e.g., Chatterjee et al., 2021; Lee et al., 2014). Second,
grounded in the behavioural perspective of the SOR model, the current
research highlighted the pivotal role that the consumer’s internal states
(i.e., cognitive processes leading to mood regulation and ow experi-
ence, the “O” component) may play in the development of compulsive
behaviours. Thus, the current research extends the existing knowledge
by afrming that both mood regulatory mechanisms and ow states may
be involved in the processes which lead to the display of compulsive
buying behaviours also in mobile online environments (Darrat et al.,
2016; Dittmar et al., 2007; Horv´
ath & Adıgüzel, 2018; Müller et al.,
2012), among Gen Z individuals. Mood regulation seems to have a
secondary role in prompting compulsive behaviour. In the symmetrical
model, mood regulation affects indirectly online compulsive buying
only via ow. Furthermore, in the fsQCA results, only one solution dis-
played mood regulation as a core condition for the presence of online
compulsive buying. Thus, experiencing ow states while using the
smartphone seem more critical in prompting compulsive behaviour than
the mood regulatory mechanism.
The trivial impact of smartphone addiction on compulsive buying in
the mediation model when the organism component are considered and
the fact that, in the fsQCA the sole presence of smartphone addiction
does not sufce for online compulsive buying to be present, offer both
some support to the claim that the smartphone per se may not be
addictive, but it allows individuals to access platforms and other content
through which they can foster their maladaptive behaviours (Grifths
et al., 2016).
Overall, our study contributes to the existing literature by identifying
the consequences of smartphone addiction, particularly compulsive
buying among Gen Z individuals. This adds to what was found in pre-
vious studies on the dark side of the use of technology, as previous
studies explored the impact of smartphone addiction on academic per-
formance (Chaudhury et al., 2018), stress, performance, and satisfaction
with life (Samaha & Hawi, 2016), family conict and personal conict
(Mahapatra, 2019). Furthermore, our study extends the ndings of these
studies by exploring another consequence of smartphone addiction.
Finally, this research extends the theoretical lens of the S–O-R model to
the smartphone addiction context, methodologically applying fsQCA to
the eld of consumers’ behaviour and proposes an adaptation of the
compulsive buying scale to the context of online purchases.
6.2. Practical implications
The ndings of this research also have a few practical implications.
First, both public and private sectors should pay increasing attention to
Gen Z individuals who are likely to indulge in compulsive buying be-
haviours online. Gen Z compulsive buyers act without considering the
nancial consequences of their behaviours. Avoiding nurturing such
compulsion must be regarded as a priority. Firms - especially those
involved in online retail - should pay increasing attention to dysfunc-
tional Gen Z customers on their platforms and take corrective actions if
needed. For example, companies may want to develop innovative al-
gorithms to identify them and enact precautional procedures (e.g.,
remove compulsive buyers from shopping newsletters, introduce a limit
of spendable money in a given timeframe). Second, public institutions –
especially those involved in education - may develop ad hoc programs
aimed at educating Gen Z and other young individuals about 1. the risks
that are related to smartphone addiction and overattachment (e.g., loss
of concentration, compulsive buying, technostress) and 2. the proper
management of their nances. All of this may contribute to stem
compulsive buying behaviours. Furthermore, from a CSR perspective,
rms may see compulsive buying among Gen Z as an opportunity to
enact socially responsible actions and interventions and, consequently,
M.C. Mason et al.
Computers in Human Behavior 136 (2022) 107404
11
picture themselves in a better light in front of their new consumers.
Overall, ethical and socially responsible marketing able to reduce the
risks of inducing compulsive buying and spread the culture of respon-
sible spending habits is utterly needed. This is important to consider Gen
Z individuals, as their available income is low – or even parents-
dependent - and their economic future is paved with uncertainty.
7. Limitations and future research
The current study has limitations that also provide fruitful avenues
for future research. One limitation is that this research evaluated
smartphone addiction without assessing usage intensity. Future research
would benet from using temporal data (e.g., daily screen time) in terms
of reliability. Still, it should also be noted that smartphone addiction is a
more complex condition that may not necessarily be well-reected in
objectively measured smartphone usage time. Therefore, relying on the
assumption that excessive smartphone use is linearly related to smart-
phone use data may lead to a biased perspective. It should also be
interesting to test if the results are replicable across different digital
technologies (e.g., computer games, which have started to include
microtransaction systems to stimulate in-game purchases). Further
studies should replicate the analysis using samples from other cultural
backgrounds as specic consumption behaviours may emerge from
different cultures. Another avenue for future research is comparing the
proposed model for different socio-demographic segments. The model
has been developed and tested on Gen Z individuals. Studying other age
groups may add signicant knowledge about the relationship between
smartphone addiction and online compulsive buying. More than this,
the model focuses on technology-related variables. Future investigations
may consider studying the interaction between individual-related
characteristics (e.g., narcissism, materialism, self-esteem) and technol-
ogy. Finally, this research is based on the cross-sectional method,
limiting the results’ generalizability. Nevertheless, the systematic and
randomised sampling approach partially offset this limitation. Using a
random sample with a high response rate to the interviewer-
administered questionnaire can be considered an indicator of the high
external validity of results, which means that the results may be deemed
generalisable at least to Western-European Gen Z individuals. Never-
theless, future scholars should focus on qualitative and longitudinal data
collection to more accurately frame the mechanisms that lead to online
compulsive buying.
8. Conclusion
This research aimed to analyse the link between smartphone addic-
tion and compulsive buying behaviours among Gen Z individuals. The
model developed in the study drew on the stimulus-organism-response
framework, and it was empirically tested using data collected through
a survey distributed to Gen Z consumers. The ndings indicated that
smartphone addiction and online compulsive buying are related, and
mood regulatory behaviour and ow experience strengthen this rela-
tionship. Accordingly, the study offers theoretical contributions and
practical implications to academics and practitioners.
Author credit statement
Michela Cesarina Mason: Conceptualization, study design, literature
review.
Gioele Zamparo: Conceptualization, study design, literature review,
data analysis.
Andrea Marini: Conceptualization, literature review.
Nisreen Ameen: R´
evising and editing of multiple versions of the
paper, literature review.
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