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Prevalence and Determinants of Compulsive Buying: A Systematic Review
with Preventive Implications
Prevalencia y determinantes de la compra compulsiva: una revisión
sistemática con implicaciones preventivas
Judith Sol-Gámez
1
, Guadalupe Molinari Conde
1
, Andrea Vázquez-Martínez
1
and Víctor José
Villanueva-Blasco*
1
1
Facultad de Ciencias de la Salud, Universidad Internacional de Valencia.
* Corresponding author: vjvillanueva@universidadviu.com
https://doi.org/10.26754/ojs_ais/accionesinvestigsoc.20254610968
Received 2024-08-05. Accepted 2024-11-15
Abstract
Introduction: Numerous studies examine the variables of compulsive buying (CB), but there is no
systematic review unifying them. The aim of this study is to analyse the international prevalence rates
of CB, establish a risk profile and examine its risk and protective factors to enable preventive
intervention. Material and Methods: A systematic literature review was conducted using PubMed,
Cochrane, Web of Science, ProQuest, and Scopus, covering the last 11 years and following PRISMA
methodology. The search was performed between November 2023 and May 2024. A total of 52
international studies were reviewed, with an average methodological quality of 85.38% according to
MMAT criteria. The main biases found in the studies were selection bias and non-response bias.
Results: Results indicate that 74% of individuals with compulsive buying behaviour were women aged
18 to 30 years, and various variables can predict this issue. CB is associated with substance addiction,
workaholism, and problematic use of the internet and social media. Discussion: In order to standardise
prevalence rates internationally, a validated and unified measurement tool must be developed.
Further research is needed to investigate environmental risk factors as well as the relationship
between CB and other addictions. It is essential to recognize this disorder with the diagnostic
importance it deserves to facilitate preventive measures. Theoretical and practical implications of the
findings are discussed in terms of designing preventive programs.
Key words: Compulsive buying; systematic review; risk factor; protective factor; prevention.
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Resumen
Introducción: Existen múltiples estudios que examinan las variables de la compra compulsiva (CC),
pero no una revisión sistemática que los unifique. El objetivo de este estudio es analizar el alcance
internacional de la CC, establecer un perfil de riesgo y examinar sus factores de riesgo y protección
para poder intervenir a nivel preventivo. Material y métodos: Se llevó a cabo una revisión sistemática
de la literatura en PubMed, Cochrane, Web Of Science, ProQuest y Scopus, referida a los últimos 11
años y siguiendo la metodología PRISMA. La búsqueda se realizó entre noviembre de 2023 y mayo de
2024. Se revisaron 52 estudios internacionales, con una calidad metodológica media del 85,38% según
los criterios MMAT. Los sesgos principales con los que contaron los estudios son el de selección y de
no respuesta. Resultados: Los resultados evidencian que el 74% de las personas con compra
compulsiva eran mujeres entre 18 y 30 años y existen diversas variables que pueden predecir esta
problemática. La CC se relaciona con la adicción a sustancias, el workaholism y los usos problemáticos
de internet y redes sociales. Discusión: Para poder estandarizar las prevalencias a nivel internacional,
se debe validar un instrumento único de medición. Se deben continuar investigando los factores de
riesgo ambientales, así como la relación de la CC con otras adicciones. Es necesario otorgar a este
trastorno la importancia diagnóstica que merece para poder prevenir su desarrollo. Se discuten las
implicaciones teóricas y prácticas de los hallazgos en cuanto al diseño de programas preventivos.
Palabras clave: Compra compulsiva; revisión sistemática; factor de riesgo; factor de protección;
prevención.
INTRODUCTION
Oniomania (Kavitha, 2017), or compulsive buying (CB), is characterised by the repetitive behaviour of
purchasing and an inability to control the impulse to buy (Martín & Pérez, 2007). It is a type of
irrational buying related to obsessive or uncontrollable buying behaviour, leading to compulsive and
excessive spending far beyond one’s actual needs (Acerit et al., 2022). In their meta-analysis, Maraz
et al. (2015) estimate the prevalence at 5%, with variations between countries and populations, and
around 80% of those affected are young women.
Miltenberger et al. (2003) highlight these negative emotions, such as anxiety, boredom, and self-
criticism, are common antecedents of CB, while emotional relief and euphoria are its primary
consequences. Low self-esteem (Belmonte et al., 2013; Gopal, 2014), materialism (Redine et al., 2023;
Troisi et al., 2006; Zhang et al., 2018), life dissatisfaction, using consumption as an escape, and stress
relief (Acerit et al., 2022; Roberts et al., 2006) are also associated with CB.
The recent systematic review by Thomas et al. (2023) validates the I-PACE model (Interaction Person-
Affect-Cognition-Execution) by Brand et al. (2019) to understand CB, by associating factors that
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generate gratification and compensation with the buying behaviour. According to Trotzke et al. (2017),
the interaction between personal factors (stress or negative moods) and situational factors
(advertisements or shopping images) increases the likelihood that compulsive buyers will react
impulsively when purchasing. Digital immediacy and hyperconnectivity encourage impulsive and ill-
considered consumption habits due to the speed and anonymity of the process (Kukar-Kinney et al.,
2009), generating instant gratification and avoiding social judgement (Huang et al., 2022), similar to
addiction models emphasising the loss or reduction of control over the context (Koob & Volkow,
2016). Other digital media-related variables that may amplify the propensity for impulsive buying
include user interfaces designed for quick purchases, the use of social media, and new marketing
strategies (Singh et al., 2023), as well as gamification, which incorporates game elements to reinforce
buying behaviour (Redine et al., 2023).
Although CB shares many characteristics with addictive disorders, it is not considered an addiction
and is excluded from the diagnostic criteria of the DSM-5 and ICD-11, lacking a preventive framework
like that of gambling or internet addiction, video games, and mobile phones. In this regard, no prior
systematic review has been conducted on CB and its predictive variables on an international scale.
Based on the established variables, the research question posed was: What are the international
prevalence rates of CB, what is its current risk profile, and what are the associated risk and protective
factors? Thus, the main objective was to establish the risk and protective factors related to CB and
define a risk profile for CB. As a secondary objective, the aim was to estimate the prevalence of CB
according to the available studies.
METHODS
Search Strategy and Information Sources
This systematic review followed the PRISMA Declaration (Preferred Reporting Items for Systematic
Reviews and Meta-Analyses; Page et al., 2021). The search was conducted between November 2023
and May 2024. The databases PubMed, Cochrane, Web of Science, ProQuest, and Scopus were
reviewed, and the study was registered in PROSPERO (CRD42024555144).
For the search strategy, the PICO method was applied, including studies with samples of individuals
exhibiting a pattern of CB (P), not undergoing any intervention (I), without a control group (C), and
that assessed prevalence or risk and protective factors, both individual and environmental (O). The
search combined DeCS/MeSH terms and free-text words related to the objectives, along with a
manual search based on the references of studies that met the eligibility criteria. The search
algorithms are presented as Supplementary Material (see Table C1).
Eligibility Criteria
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During the study selection process, the same filters were applied across all databases, filtering the
search by (1) open-access and full-text articles; (2) publications from the last 11 years (2013-2024),
considering the relevance and trend changes over the past decade in the shift from in-person to online
shopping; (3) articles published in English or Spanish; and (4) descriptive studies, scientific journal
articles, doctoral theses, master's theses, reports, systematic reviews, clinical trials, randomised
controlled trials, and meta-analyses. Grey literature was included in this research due to the lack of
peer-reviewed articles on the study topic. Studies focused exclusively on the treatment of CB or that
did not investigate any of the four defined variables were excluded.
Selection Process
In the first search, the set of databases yielded a total of 584 results, to which 8 manually identified
studies were added, resulting in a total of 592 records. After removing duplicates, 540 studies
remained, of which 488 were excluded for (a) focusing on the treatment of CB (n=52) or (b) not
addressing the research variables (n=436). Finally, 52 studies were selected for abstract review, and
all were included in the review (Figure 1).
Figure 1
Flow Diagram of Information According to the Phases of the Systematic Review (PRISMA)
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Data Extraction
Data extraction was carried out through peer review and without the use of automation tools. The
established variables of interest for data collection were the following: age, sex, risk factors (RF) and
protective factors (PF), in both cases endogenous and exogenous. After the selection of articles was
completed, they were coded based on the following information: (a) Authors and year, (b) country,
(c) study design, (d) sample, (e) study variables, (f) scale used to assess CB, and (g) main findings.
Methodological Quality Assessment
The methodological quality of the studies was assessed using the Mixed Methods Appraisal Tool
(MMAT; Hong et al., 2018). The MMAT is an assessment tool designed for systematic reviews that
include empirical quantitative, qualitative, and mixed studies.
RESULTS
Of the 52 studies included, 51 were descriptive quantitative studies (47 cross-sectional and 5
longitudinal) and one was a mixed-methods study. The methodological quality assessment using the
MMAT (Hong et al., 2018) is provided as Supplementary Material (see Tables C2 and C3). All studies
met at least 60% of the criteria, with an average compliance rate of 85.38%. The main biases identified
were selection bias and nonresponse bias.
Characteristics and Results of the Analysed Studies
Table 1 presents the characteristics and main findings of the analysed studies, followed by a
description of the specific results related to the objectives established for the present study.
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Table 1
Characteristics of the Analysed Studies
Authors and
year
Country
Study design
Sample
Scale used to
assess CB
Main findings
Aadel et al.
(2023)
United
States
Cross-sectional
N=193 (56% M & 44% F).
Ages 18-24.
Faber & O’Guinn
(1992)
14.5% exhibit CB with no differences by sex. Depression does not
directly affect CB, but it does affect obsessive-compulsive
behaviours, which are influenced by materialism.
Adamczyk, G.
(2021)
Poland
Cross-sectional
N=1000 (47,8% M &
52,1% F). Ages +15.
German
Compulsive Buying
Scale (GCBS; Raab
et al., 2005)
Prevalence of 3.4%. Online shoppers, especially women, are more
susceptible to CB. The frequency of online shopping does not
explain CB.
Adamczyk et al.
(2022)
Poland
Cross-sectional
N=1121 (50,3% F &
49,7% M) M= 46,6 years.
GCBS (Raab et al.,
2005)
People with prosocial attitudes, but with low self-esteem and
materialistic values, tend to develop CB.
BAdamczyk, G.
(2024)
Poland
Cross-sectional
N=1000 (52% F & 48%
M). Age +18 (samples
2010, 2019 & 2022)
GCBS (Raab et al.,
2005)
Stable prevalence (2% in 2010 and 4% in 2022) since 2010, more
prevalent in online shopping. Materialism as a RF. The
hospitalisation of a friend due to COVID-19 is a more relevant
predictor in women than age.
Baltaci & Eser
(2022)
Turkey
Cross-sectional,
Mixed Methods
Qualitative design: N=52
(33 M & 19 F) M= 34
years. Quantitative
design: N=776.
-
Greater tendency to CB among those aged 18 to 28 years.
Environmental factors influencing purchasing decisions are
defined. PF: shopping in company.
Biolcati (2017)
Italy
Cross-sectional
N=240 (170 F & 70 M)
M= 33 years.
Valence et al.
(1988)
No sex differences. Contingent self-esteem predicts CB, especially
in women. People with CB shop more often, with no differences in
spending, credit cards, sex, or age.
Bobby &
Zakkariya
India
Cross-sectional
N=350 (55,2% M &
44,8% F) ages 18-40 &
d’Astous et al.
(1990)
Adults on social media, more materialistic and anxious, are more
likely to develop CB. Reducing their time on social media could
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(2024)
M= 28,5 years.
improve well-being and prevent CB.
Brook et al.
(2015)
United
States
Longitudinal
N=528 (55% F & 45% M),
followed from age 14
until age 43.
Valence et al.
(1988)
Adolescents who identify with their parents have less depression
and a lower risk of CB in adulthood (more common in women).
Drug use, impulsive buying, and ADHD are associated with CB in
adults.
Burhan et al.
(2022)
Malaysia
Cross-sectional
N=439 (75,6% F & 24,4%
M) ages 18-29.
Compulsive Online
Shopping Scale
(COSS; Manchiraju
et al., 2017)
Through materialism, the attributes of the Big Five model—
neuroticism, openness to experience, and conscientiousness—
influence CB.
Castellanos et
al. (2020)
Chile
Cross-sectional
N=423 (54,4% F & 45,6%
M) M= 15 years.
Attitudes Towards
Buying Scale (Luna-
Arocas & Fierres,
1998)
FR: materialism. Inverse relationship between CB and life
satisfaction, and between life satisfaction and materialism.
Challet-Bouju et
al. (2020)
France
Cross-sectional
N=242 (100% F) M= 25
years.
Faber & O’Guinn
(1992)
FR: low self-esteem, high impulsivity, sense of loss of control, and
coping motivation.
Cheema et al.
(2014)
Pakistan
Cross-sectional
N=400 (50% F & 50% M).
Richmond
Compulsive Buying
Scale (RCBS;
Ridgway et al.,
2008)
The attributes of the Big Five model—conscientiousness,
neuroticism, and intellect—influence CB.
Eroğlu y
Kocatürk (2020)
Turkey
Cross-sectional
N=1000. Ages +22.
Lee & Park (2008)
Materialism as a RF of online CB.
Estévez et al.
(2021)
Spain
Longitudinal
N=182 (56,6% F & 43,4%
M) M= 16,7 years,
followed for 1 year.
Pathological Buying
Screener (PBS;
Müller et al., 2015)
Higher prevalence of CB in women. Materialism as a RF.
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Godfred et al.
(2023)
Ghana
Longitudinal
N=477 (55,3% F & 44,7%
M) with the majority
aged 18-22 years.
Faber &
Christenson (1996)
CB is associated with loan dependency. Students with limited time
suffer from anxiety and insecurity, which are FR. Financial
management skills can act as PF.
González &
Lemos (2020)
Colombia
Cross-sectional
N=98 (67 F & 31 M) M=
20 years.
Bergen Shopping
Addiction Scale
(BSAS; Andreassen
et al., 2015)
Prevalence of CB is 23.5% (29.9% in women and 9.7% in men).
Impulsivity and anxiety as RF.
Grougiou et al.
(2015)
Greece
Cross-sectional
N=285 (49,5% F & 50,5%
M) M= 22,8 years.
Roberts et al.
(2003)
CB is linked with disruptive family experiences and low
socioeconomic status. Parenting in extended families can reduce
CB, while peer dependence in adolescence increases CB in
adulthood.
Harnish et al.
(2016)
United
States
Cross-sectional
N=297 (59% F & 41% M)
M= 19 years.
RCBS (Ridgway et
al., 2008)
Prevalence 17.51%. CB in 27% of women and 3% of men. Irrational
beliefs, such as excessive worry, problem avoidance, and the need
for approval, predict CB.
Harnish et al.
(2019)
United
States
Cross-sectional
N=284 (65% F & 35% M)
M= 19,5 years.
RCBS (Ridgway et
al., 2008)
CB is not linked with loneliness, but materialism, payment pain,
social anxiety (especially anhedonia), substance use, and family
social support are.
Jaamel et al.
(2024)
Saudi
Arabia
Cross-sectional
N=419 (51,2% M &
48,8% F).
Valence et al.
(1988)
Obsessive use of social media and TV ads are linked to CB in college
students, mediated by materialism.
Jiang & Shi
(2016)
China
Cross-sectional
N=601. M= 20,63 years.
Faber & O’Guinn
(1992)
Prevalence of CB is 5.99% in students, no difference by sex. Those
who shop more are more vulnerable, and CB is linked to
problematic internet use, low self-esteem, and self-efficacy.
Kirezli & Arslan
(2019)
Turkey
Cross-sectional
N=372 (60,8% F & 39,2%
M)
d’Astous et al.
(1990) and Faber &
O’Guinn (1992)
Escapism, hedonism, and reduction of negative mood states are
the main factors that, when combined, influence CB the most.
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Kovács et al.
(2022)
United
States
Cross-sectional
N=1430 (60% M & 40% F)
M= 36,6 years.
BSAS (Andreassen
et al., 2015) and
COSS (Manchiraju
et al., 2017)
Maladaptive coping, such as self-criticism or anger, mediated the
relationship between stress and CB, especially at the peak of the
COVID-19 pandemic.
Leite & Silva
(2016)
Brazil
Cross-sectional
N=359 (83% F & 17% M)
M= 31,87 years.
RCBS (Ridgway et
al., 2008)
10.3% CB. 100% prevalence in women, most affected at 32.62
years. Depression and CB are linked, but not anxiety and CB.
Long et al.
(2021)
China
Cross-sectional
N=539 (52,8% F & 47,2%
M) M= 20,32 years.
d’Astous et al.
(1990)
Excessive use of social media as a FR for online CB. 87% of the
sample spends over 1 hour per day on social media.
Maccarrone-
Eaglen &
Schofield (2017)
United
Kingdom
Cross-sectional
N=776 (65,7% F & 34,3%
M) M= 18-24 years.
United Kingdom, Spain,
Czech Republic & China.
Valence et al.
(1988) and Faber &
O’Guinn (1989)
CB is not dependent on cultural individualism/collectivism. Loss of
control over spending (SIS) supports addiction classification. SIS is
more frequent in women in the UK and Spain.
Mikołajczak-
Degrauwe &
Brengman
(2014)
Belgium
Cross-sectional
N=582 (68,8% F & 31,2%
M) M= 43 years.
RCBS (Ridgway et
al., 2008)
Prevalence of 8.5%. Compulsive buyers feel more influenced by
advertising, especially those less knowledgeable about
persuasion.
Nori et al.
(2022)
Italy
Cross-sectional
N=105 (65 F & 40 M) M=
35,18-36,15 years.
Faber & O’Guinn
(1989)
During the COVID-19 pandemic, buying patterns changed.
Nyrhinen et al.
(2023)
Finland
Cross-sectional
N=1000 (52% M & 49% F)
ages 18-29 years.
Online Shopping
Addiction Scale
(OSAS; Zhao et al.,
2017)
Less self-regulation intensifies online CB, especially if there are
problems with phone use. Low self-regulation and indebtedness
are linked to CB.
Otero-López et
al. (2017)
Spain
Cross-sectional
N=2159 (51,9% F & 48,1
M) M= 35,4 years.
GCBS (Raab et al.,
2005)
CB is linked to extrinsic goals and neuroticism (Big Five). Preventive
programs should address life goals and negative emotions.
Otero-López et
al. (2021a)
Spain
Cross-sectional
N=1093(52,2% F & 47,8%
M) M= 19,49 years.
GCBS (Raab et al.,
2005)
CB is linked to neuroticism (Big Five). RF: passive coping strategies.
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Otero-López et
al. (2021b)
Spain
Longitudinal
N=527 (53,5% F & 46,5%
M) M= 18,61 years.
GCBS (Raab et al.,
2005)
Students at high risk for CB perceive their life projects as stressful
and less meaningful (low self-efficacy). People with CB feel less
control and adjustment in their projects.
Otero-López et
al. (2024)
Spain
Cross-sectional
N=487 (43,9% M &
56.1% F) M= 18,92 years.
GCBS (Raab et al.,
2005)
CB is linked to neuroticism and low agreeableness (Big Five).
Material purchases may attempt to maintain self-esteem if low
levels are present.
Otero-López &
Villardefrancos
(2014)
Spain
Cross-sectional
N=2159 (51,9% F & 48,1
M) M= 35,4 years.
GCBS (Raab et al.,
2005)
Prevalence of 7.1%, more common in young women. RF: anxiety,
depression, and passive coping strategies. PF: older age and active
strategies.
Pahlevan Sharif
& Ken Kyid
(2018)
Malaysia
Cross-sectional
N=1150 (56,4% F &
43,6% M) M= 20,55
years.
d’Astous et al.
(1990)
Positive association between excessive social media use and
online CB. FR for online CB: power, prestige, and money anxiety.
Pérez et al.
(2021)
Spain
Cross-sectional
N=573 (50,7% F & 49,3%
M) M= 15,65 years.
Self-developed
instrument
(Cronbach’s α =
0.848)
CB is more prevalent in women. FR: substance use (cannabis,
alcohol, and cannabis and alcohol alone).
Rachubińska et
al. (2022)
Poland
Cross-sectional
N=556 (100% F) M= 34
years.
BSAS (Andreassen
et al., 2015)
Loneliness does not significantly predict CB. RF: depression.
Rachubińska et
al. (2024)
Poland
Cross-sectional
N=556 (100% F)
BSAS (Andreassen
et al., 2015)
RF: depression, neuroticism, cognitive restriction of eating,
uncontrolled eating, and work addiction. CB profile shows lower
scores in agreeableness and conscientiousness. 26% with CB show
work addiction.
Roberts et al.
(2019)
United
States
Cross-sectional
N=1289 (732 F & 552 M)
aged 12-18 years.
d’Astous et al.
(1990)
Family conflict affects CB through materialism in women and self-
esteem in both sexes. The relationship between family conflict and
self-esteem is more pronounced in women.
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Rocha et al.
(2023)
Portugal
Cross-sectional
N=365 (72,15% F &
27,95% M) M= 22,41
years.
RCBS (Ridgway et
al., 2008)
Maladaptive schemas as RF, especially hypervigilance and
inhibition. Greater propensity for CB in women.
Santiago &
Castro (2021)
Spain
Cross-sectional
N=1093. M= 19,49 years.
BSAS (Andreassen
et al., 2015)
RF: extrinsic goals (image, financial success, popularity, and
conformity). PF: intrinsic goals (self-acceptance, affiliation, and
community feeling). Sex (F) as RF.
Šeinauskienė et
al. (2021)
Lithuania
Cross-sectional
N=724 (64,2% F & 35,8%
M) M= 23 years.
Edwards
Compulsive Buying
Scale (ECBS;
Edwards, 1993)
Students with lower intelligence scores (related to emotional
management) showed a greater propensity for CB.
Singh & Nayak
(2016)
India
Cross-sectional
N=300 (65,66% M &
34,33% F) M= 16,66
years.
Roberts et al.
(2003)
The quality of intergenerational relationships in adolescence is
crucial for self-identity and self-esteem. More conflict and less
family cohesion reduces adolescent self-esteem (RF).
Topino et al.
(2022)
Italy
Cross-sectional
N=306 (76% F & 24% M)
M= 31 years.
COSS (Manchiraju
et al., 2017)
Secure attachment has a positive effect on family cohesion (PF).
RF: insecure attachment due to disorganised family functioning.
Tran et al.
(2023)
Vietnam
Cross-sectional
N=664 (70,9% F & 28%
M) M= 21,95 years.
RCBS (Ridgway et
al., 2008)
Social status and self-compassion do not directly affect CB, but
they moderate the relationship between depression and CB.
Ünübol et al.
(2022)
Turkey
Cross-sectional
N=24380 (12249 M &
12131 F) M= 31,79 years.
Self-developed
instrument
(Cronbach’s α =
0.9)
Prevalence 1.8%. Pre-pandemic context. RF: being a young
woman, psychiatric distress, negative affect, insecure attachment,
and avoidant attachment.
Valero-Solís et
al. (2018)
Spain
Cross-sectional
N=113 (75,2% F & 24,8%
M), +18 years, treated by
CB.
McElroy et al.
(1994)
RF: sex (F). Early onset of CB linked to high dependence on rewards
and low self-transcendence in women; low self-direction and
cooperation in men.
Villardefrancos
& Otero-López
(2016)
Spain
Cross-sectional
N=1448 (50,4% F &
49,6% M) M= 19 years.
GCBS (Raab et al.,
2005)
Prevalence of CB 7.4%. FR: materialism and psychological distress
(anxiety, depression, obsession-compulsion, somatization, and
hostility). PF: life satisfaction.
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Wang & Zhai
(2022)
China
Cross-sectional
N=622 (313 from China &
309 from United States
(54,7% F & 45,3% M) +18
years.
ECBS (Edwards,
1993)
RF: materialism in both countries. The US shows more CB than
China (long-term orientation may act as PF).
Xu et al. (2022)
Germany
Cross-sectional
N=1038. +14 years.
GCBS (Raab et al.,
2005)
Prevalence 6.8% (more women). RF: online shopping, no
distinction by sex. FR: electronic payment systems (especially for
women).
Ye et al. (2021)
China
Cross-sectional
N=2439 (52,2% F &
47,8% M) aged 18-59
years.
RCBS (Ridgway et
al., 2008)
Prevalence: 18.5% among 30-39 years, 11.3% among 18-29 years,
and 8.5% among 40-59 years.
Zhang et al.
(2016)
United
States
Longitudinal
N=548 (55% F & 45% M)
M= 43 years. Followed
from adolescence, 7
interviews (1983-2013).
Valence et al.
(1988)
CB linked to substance abuse, MDE, and GAD at 43 years.
Preventing CB may be PF for those variables. The study focuses on
an older sample, unlike studies on youth.
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Prevalence
Prevalence rates vary when compared by geographic areas. Studies with general population samples
show higher rates in Asia and South America compared to Europe. Within Europe, higher prevalence
is observed in Central European countries compared to those in the South. Additionally, CB affects
women more significantly in Brazil, Poland, Germany, and Spain. The available data from China
indicates a general weighted prevalence of 12.41%, with higher rates in early adulthood (Ye et al.,
2021), followed by Brazil at 10.30% (Leite & Silva, 2016), Belgium at 8.5% (Mikołajczak-Degrauwe &
Brengman, 2014), Spain at 7.1% (Otero-López & Villardefrancos, 2014), Germany at 6.8% (Xu et al.,
2022), and Poland at 4% (Adamczyk, 2024).
On the other hand, student samples show a higher prevalence of CB in this segment of the population,
especially in South and North America, where it is also more prevalent among women, compared to
Asia and Europe. Specifically, prevalence data is available for Colombia at 23.5% (González & Lemos,
2020), the United States (Aadel et al., 2023) at 14.5%, China at 11.3% (Ye et al., 2021), and Spain at
7.4% (Villardefrancos & Otero-López, 2016).
Figure 2
Distribution of Prevalence by Geographic Areas and Population Groups
Note: Data labels in orange indicate higher prevalence of CB in women. Data labels in black indicate no sex differences. Colombia: González
& Lemos, 2020. Brazil: Leite & Silva, 2016. US: Aadel et al., 2023. China: Ye et al., 2021. Turkey: Ünübol et al., 2022. Poland: Adamczyk, 2024.
Germany: Xu et al., 2022. Belgium: Mikołajczak-Degrauwe & Brengman, 2014. Spain (general population): Otero-López & Villardefrancos,
2014. Spain (student population): Otero-López & Villardefrancos, 2016.
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Vulnerable populations to Compulsive Buying
Women are more vulnerable to CB (Adamczyk, 2021, 2024; González & Lemos, 2020; Harnish et al.,
2016; Otero-López & Villardefrancos, 2014; Pérez et al., 2021; Rocha et al., 2023; Ünübol et al., 2022;
Valero-Solís et al., 2018; Xu et al., 2022). This finding is reinforced by two longitudinal studies (Brook
et al., 2015; Estévez et al., 2021), in contrast to others that found no sex differences (Aadel et al.,
2023; Biolcati, 2017; Jiang & Shi, 2016). Leite & Silva (2016) found that all compulsive buyers in their
sample were women around 30 years old. Maccarrone-Eaglen & Schofield (2017) indicate that the
Self-control Impaired Spending (SIS) dimension, marked by the inability to exercise self-control and
intensified by the compulsion to act (buy) to relieve anxiety, has a higher incidence among women.
Young individuals also show vulnerability to CB (Aadel et al., 2023; Baltaci & Eser, 2022; González &
Lemos, 2020; Harnish et al., 2016; Jiang & Shi, 2016; Villardefrancos & Otero-López, 2016; Ye et al.,
2021).
Endogenous Risk Factors
Materialism is directly associated with CB in both virtual and physical environments (Adamczyk, 2022;
Castellanos et al., 2020; Eroğlu & Kocatürk, 2020; Harnish et al., 2019; Wang & Zhai, 2022). In all
consumers, it increases depression and triggers obsessive-compulsive behaviours (Aadel et al., 2023),
but specifically among young consumers, materialism can also create a discrepancy between the
desired and the actual self, prompting purchases to reinforce identity (Jiang & Shi, 2016).
Neuroticism and conscientiousness from the Big Five personality traits are also predictive variables
(Burhan et al., 2022; Cheema et al., 2014; Otero-López et al., 2024): high levels of neuroticism erode
self-esteem, increasing vulnerability (Otero-López et al., 2024). In line with this, low self-esteem
predicts CB (Jiang & Shi, 2016; Harnish et al., 2019), especially in women (Biolcati, 2017). Even
individuals with a prosocial attitude may show a tendency toward CB if their self-esteem is low
(Adamczyk, 2022).
The reduction of negative moods is one of the primary motivations for CB (Challet-Bouju et al., 2020;
Kirezli & Arslan, 2019). Additionally, CB is linked to passive coping strategies (Kovács et al., 2022;
Otero-López & Villardefrancos, 2014; Otero-López et al., 2021a), particularly as impulsivity increases
(Challet-Bouju et al., 2020; González & Lemos, 2020).
Irrational beliefs, such as excessive or distressing worry, avoidance of problems, and the need for
approval, are strong predictors of CB (Harnish et al., 2016). Likewise, maladaptive schemas,
particularly the schema of over-vigilance and inhibition (Rocha et al., 2023), and life goals related to
image, popularity, and financial success are influential, as they prioritise extrinsic goals to achieve life
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satisfaction (Otero-López et al., 2017; Santiago & Castro, 2021).
Another risk factor is anxiety (González & Lemos, 2020; Otero-López & Villardefrancos, 2014;
Villardefrancos & Otero-López, 2016), particularly among students (Godfred et al., 2023), while no
significant relationship has been found with depression (Leite & Silva, 2016; Zhang et al., 2016) or
loneliness (Harnish et al., 2019; Rachubińska et al., 2022). The perception of stressful and unfulfilling
life projects in students impacts their self-efficacy, increasing the risk of CB (Otero-López et al., 2021b).
Compulsive buying is associated with substance abuse (Brook et al., 2015; Pérez et al., 2021; Zhang et
al., 2016) and workaholism (Rachubińska et al., 2024). The study by Rachubińska et al. (2024),
conducted with 556 Polish women, found that workaholism was present in 26% of compulsive buyers.
Elevated scores were also recorded in cognitive restriction of eating and uncontrolled eating.
Additionally, lower levels of self-transcendence were observed in women (Valero-Solís et al., 2018).
Finally, it is important to note the link between compulsive buying (CB) and problematic internet use
(Jiang & Shi, 2016; Nyrhinen et al., 2023), excessive social media use (Bobby & Zakkariya, 2024; Jaamel
et al., 2024; Long et al., 2021; Pahlevan-Sharif & Ken-Kyid, 2018), and problems with regulating
smartphone usage (Nyrhinen et al., 2023). Pahlevan-Sharif and Ken-Kyid (2018) highlighted the role
of power-prestige attributed to money and monetary anxiety as mediators of the relationship
between social media use and CB. Bobby and Zakkariya (2024) demonstrated that social media users
tend to be more materialistic and socially anxious, making them more likely to engage in compulsive
buying. In other words, exposure to idealised lives on social media affects attitudes toward money,
leading to CB as individuals attempt to align their real personal image with the ideal.
The endogenous risk factors predicting CB, listed in order of empirical support, are shown in Table 2.
Table 2
Main Endogenous Risk Factors predicting Compulsive Buying, ranked from highest to lowest empirical support
Endogenous
Risk Factor
Empirical Support
Cross-Sectional Studies
Longitudinal
Studies
Mixed-Design
Studies
Materialism
Harnish et al. (2019); Castellanos et al. (2020);
Eroğlu & Kocatürk (2020); Adamczyk (2022);
Burhan et al. (2022); Wang & Zhai (2022); Aadel
et al. (2023); Adamczyk (2024); Bobby & Zakkariya
(2024)
-
-
Anxiety
Otero-López & Villardefrancos (2014);
Villardefrancos & Otero-López (2016); González &
Lemos (2020); Nori et al. (2022)
Godfred et al.
(2023)
-
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Low self-esteem
Jiang & Shi (2016); Biolcati (2017); Harnish et al.
(2019); Challet-Bouju et al. (2020); Adamczyk
(2022)
-
-
Neuroticism
(Big Five)
Cheema et al. (2014); Otero-López et al. (2017);
Otero-López et al. (2021a); Burhan et al. (2022);
Otero-López et al. (2024); Rachubińska et al.
(2024)
-
-
Excessive use of social
media
Pahlevan-Sharif & Ken-Kyid (2018); Long et al.
(2021); Bobby & Zakkariya (2024); Jaamel et al.
(2024)
-
-
Substance use
Pérez et al. (2021)
Brook et al.
(2015); Zhang et
al. (2016);
-
Low self-transcendence
Valero-Solís et al. (2018)
Otero-López et al. (2024)
Otero-López et al.
(2021b)
-
Irrational beliefs
Harnish et al. (2016); Kirezli & Arslan (2019);
Kovács et al. (2022)
-
-
Scrupulosity
(Big Five)
Cheema et al. (2014); Burhan et al. (2022); Otero-
López et al. (2024)
-
-
Social comparison
Jiang & Shi (2016); Aadel et al. (2023)
-
-
Passive coping
strategies
Otero-López & Villardefrancos (2014); Otero-
López et al. (2021a)
-
-
Online shopping
Adamczyk (2021); Adamczyk (2024)
-
-
Impulsivity
Challet-Bouju et al. (2020); González & Lemos
(2020)
-
-
Extrinsic life goals
Otero-López et al. (2017); Santiago & Castro
(2021)
-
-
Problematic internet
use
Jiang & Shi (2016); Nyrhinen et al. (2023)
-
-
Workaholism
Rachubińska et al. (2024)
-
-
Depression
Rachubińska et al. (2024)
-
-
Maladaptive schemas
Rocha et al. (2023)
-
-
Exogenous Risk Factors
Various environmental factors, such as colours, scents, music, and store temperature, influence
purchasing decisions, increasing the time consumers spend in a store and exposing them to marketing
and persuasion strategies that may lead to irrational purchases (Baltaci & Eser, 2022). Additionally,
the influence of advertising is more effective in compulsive buyers (Mikołajczak-Degrauwe &
Brengman, 2014). Furthermore, online shopping has an enhancing effect on compulsive buying
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(Adamczyk, 2021; Xu et al., 2022).
Grougiou et al. (2015) link compulsive buying with disruptive family experiences and control over
individual decisions. Other studies indicate that family conflict increases materialism and lowers self-
esteem, particularly in women (Topino et al., 2022; Ünübol et al., 2022). Additionally, insecure
attachment and dependence on peer groups during adolescence are associated with a greater
tendency toward CB in adulthood (Roberts et al., 2019; Singh & Nayak, 2016; Topino et al., 2022;
Ünübol et al., 2022).
The exogenous risk factors predicting CB, listed from highest to lowest empirical support, are
presented in Table 3.
Table 3
Main Exogenous Risk Factors predicting Compulsive Buying, ranked from highest to lowest empirical support.
Exogenous Risk Factor
Empirical Support
Cross-Sectional Studies
Longitudinal
Studies
Mixed-Design
Studies
Disruptive family environment
Grougiou et al. (2015); Roberts et
al. (2019); Singh & Nayak (2016)
-
-
Insecure attachment
Topino et al. (2022); Ünübol et al
(2022)
-
-
Internet shopping
Adamczyk et al. (2022); Xu et al.
(2022)
-
-
Retail environment
-
-
Baltaci & Eser (2022)
Peer group
Grougiou et al. (2015)
-
-
Advertising
Mikołajczak-Degrauwe &
Brengman, (2014)
-
-
Marketing tactics
-
-
Baltaci & Eser (2022)
Endogenous Protective Factors
Life satisfaction is negatively related to CB and materialistic values (Castellanos et al., 2020;
Villardefrancos & Otero-López, 2017), as well as emotional intelligence (Šeinauskienė et al., 2021) and
self-esteem (Adamczyk, 2024). Santiago and Castro (2021) demonstrated that individuals who do not
tend toward CB score highly on intrinsic goals (self-acceptance, affiliation, and community feeling),
which, along with generativity, are protective factors against CB. This finding is consistent with other
studies that show the effectiveness of active coping strategies, such as problem-solving (Nyrhinen et
al., 2023; Otero-López et al., 2021a) and self-efficacy (Otero-López et al., 2021a).
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Furthermore, knowledge of marketing tactics increases disbelief and reduces the compulsive need to
acquire advertised products (Mikołajczak-Degrauwe & Brengman, 2014), while financial management
knowledge minimises the tendency toward CB (Godfred et al., 2023) and associated debt (Nyrhinen
et al., 2023).
The endogenous protective factors that predict CB, ranked from highest to lowest empirical support,
are presented in Table 4.
Table 4
Main Endogenous Protective Factors predicting Compulsive Buying, ranked from highest to lowest empirical support.
Endogenous Protective Factor
Empirical Support
Cross-Sectional Studies
Longitudinal Studies
Mixed-Design
Studies
Life satisfaction
Castellanos et al., (2020);
Villardefrancos & Otero-López
(2017)
-
-
Active coping strategies
Otero-López et al. (2021a);
Nyrhinen et al (2023)
-
-
Knowledge of financial
management strategies
Nyrhinen et al. (2023)
Godfred et al. (2023)
-
Knowledge of marketing strategies
Mikołajczak-Degrauwe &
Brengman (2014)
-
-
Self-esteem
Adamczyk (2024)
-
-
Self-efficacy
Otero-López et al. (2021a)
-
-
Generativity
Santiago & Castro (2021)
-
-
Intrinsic life goals
Santiago & Castro (2021)
-
-
Emotional intelligence
Šeinauskienė et al. (2021)
-
-
Exogenous protective factors
Adolescents with a stronger identification with their parents have lower levels of depression,
which protects them against compulsive behaviours (Brook et al., 2015). Grougiou et al. (2015)
suggest that parenting linked to extended family can be preventive by providing various adult
references. Additionally, secure attachment strengthens family cohesion, identified as a
protective factor against CB (Topino et al., 2022). On the other hand, shopping with others can
also reduce the likelihood of irrational purchase decisions (Baltaci & Eser, 2022).
The exogenous protective factors predicting CB, presented from most to least empirical support,
are shown in Table 5.
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Table 5
Main Exogenous Protective Factors predicting Compulsive Buying, ranked from highest to lowest empirical support.
Exogenous Protective Factor
Empirical Support
Cross-Sectional Studies
Longitudinal Studies
Mixed-Design
Studies
Secure attachment
Topino et al. (2022)
-
-
Shopping with others
-
-
Baltaci & Eser (2022)
Parenting linked to extended
family
Grougiou et al. (2015)
-
-
Identification with parents
-
Brook et al. (2015)
-
DISCUSSION
The objective of the present study was to estimate the prevalence of CB, identify the risk and
protective factors related to it, and establish a risk profile for this behaviour.
The difficulty in the diagnostic conceptualization of CB may be the reason for the use of different
instruments, which can potentially bias the analyses and findings of various studies. Taking this
limitation into account, the prevalence of CB in the reviewed studies ranges from 1.8% to 12.41% in
the general population, with significant variation between countries. Higher prevalences are observed
in Asia and America. Within Europe, the rates are higher in Central European countries compared to
those in the South. These disparities could be influenced by various factors, including cultural or
socioeconomic contexts, as well as the degree of adoption of consumerist lifestyles in certain
societies. Regarding sex and age, the findings of the present study suggest that being a young woman
represents a higher vulnerability to CB. This supports the findings of previous studies (Maraz et al.,
2015; Martín & Pérez, 2007).
The escapist function of compulsive buying is proven, especially in individuals with low self-esteem or
insecure attachment, who seek approval and escape self-criticism through passive coping strategies
(Biolcati, 2017; Challet-Bouju et al., 2020; González & Lemos, 2020; Kirezli & Arslan, 2019; Topino et
al., 2022). This behaviour aligns with the self-medication hypothesis of addictive disorders (Khantzian,
1985). There is no consensus on whether the use of credit cards is a predictor of CB (Biolcati, 2017).
However, electronic payment systems reduce the perceived pain of paying, which could increase the
risk of CB (Xu et al., 2022). These systems automate the buying process and eliminate the reflective
factor, making spending intangible and thus more difficult to control.
The acquisition of material goods is used as a means to enhance social image and construct an identity
that meets societal expectations (Adamczyk, 2021; Harnish et al., 2016). Young people are particularly
vulnerable to this phenomenon, given their high exposure to virtual environments that encourage
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social acceptance seeking, combined with difficulties in decision-making and emotional regulation,
typical of the prefrontal cortex's immaturity (Gogtay et al., 2004). In this regard, dependence on peers
during adolescence, especially in relation to consumption, leads to stronger CB tendencies in
adulthood (Grougiou et al., 2015).
New evidence is added to the comorbidity of CB with substance abuse (Black et al., 2015; Brook et al.,
2015; Mestre-Bach et al., 2017; Pérez et al., 2021; Zhang et al., 2016), problematic internet use
(Nyrhinen et al., 2023; Xu et al., 2022), and workaholism (Rachubińska et al., 2024), eating disorders
(Claes et al., 2011; Rachubińska et al., 2024), impulse control disorders (Müller et al., 2010), anxiety
disorders (Müller et al., 2010; Zhang et al., 2016), low self-esteem (Belmonte et al., 2013; Gopal, 2014),
and personality disorders such as avoidant, obsessive-compulsive, and borderline (Claes and Müller,
2017). Depression is not identified as a predictive variable; however, it acts as a mediator between
loneliness and CB (Rachubińska et al., 2022).
The endogenous protective factors found for CB are life satisfaction (Castellanos et al., 2020;
Villardefrancos and Otero-López, 2017), emotional intelligence (Šeinauskienė et al., 2021), self-
regulation (Nyrhinen et al., 2023), knowledge in financial management and marketing tactics
(Mikołajczak-Degrauwe and Brengman, 2014), and the use of active coping strategies in response to
conflicts, which, together with the establishment of intrinsic life goals (Otero-López and
Villardefrancos, 2014; Otero-López et al., 2021a), foster a sense of generativity (Santiago and Castro,
2021). The findings also suggest that individuals who do not rely on others' evaluations may be at a
lower risk of developing CB (Biolcati, 2017).
There are discrepancies regarding the influence of cultural values on CB. In individualistic societies, CB
may be more common, but a long-term orientation and a broad family network can act as protective
factors (Grougiou et al., 2015; Maccarrone-Eaglen and Schofield, 2017; Wang and Zhai, 2022). A recent
study revealed that values such as self-direction, stimulation, hedonism, power, and achievement
tend to promote CB tendencies regardless of the cultural context. However, the prevalence of CB is
more pronounced among consumers from countries like the United States compared to Eastern
European countries (Tarka et al., 2024). On the other hand, values such as tradition, security,
conformity, benevolence, and universalism appeared to help individuals prevent or mitigate CB.
It is vital to address the critical use of technology and mobile phones, as online CB is associated with
the excitement and impact of the digital environment (Jebarajakirthy et al., 2021; May and Irmak,
2014; Rose and Dhandayudham, 2014, as cited in Nyrhinen et al., 2023). Therefore, preventive
programs should address discrepancies between the virtual and real images and promote a critical
analysis of advertising and marketing campaigns, as well as reflect on the consequences of unplanned
purchases (Otero-López et al., 2021b). Moreover, developing emotional intelligence can reduce
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dependence on external approval and lead to reflection on materialistic values (Šeinauskienė et al.,
2021), contributing to healthy self-esteem and life satisfaction beyond material possession. A gender
perspective should also be incorporated, considering those factors that affect women to a greater
extent (Valero-Solís et al., 2018; Biolcati, 2017; Roberts et al., 2019).
On the other hand, family prevention should focus on enhancing family support, as it reduces anxiety
and promotes self-regulation (Grougiou et al., 2015; Topino et al., 2022; Otero-López et al., 2017;
Harnish et al., 2019). At the environmental prevention level, since electronic payment underestimates
spending, policies should include messages during the online payment process that warn about the
risk of excessive consumption and indebtedness (Xu et al., 2022), similar to the health warnings on
cigarette packs. Furthermore, it is crucial to promote the ethical use of marketing and regulate
advertising on social media, especially targeting vulnerable segments, just as gambling and betting
advertising was regulated in Spain (Law 13/2011).
Limitations and Future Proposals
This systematic review is not without limitations. First, 47 of the 52 studies reviewed are cross-
sectional, meaning they do not allow for the establishment of causal relationships. Second, most of
the studies reviewed use self-report questionnaires, which may introduce biases related to memory
recall or social desirability. It would be beneficial to use tools that control for these biases (Valero-
Solís et al., 2018). Third, the convenience samples are concentrated among university students, which
may limit the generalisation of findings to other broader populations. However, the sample sizes are
considerably large. Therefore, the results should be interpreted with caution, as the studies included
in this review differ in terms of the methodological design used, sample size and profile, and
measurement scales.
The identified limitations open the door to future research avenues. For example, having more
longitudinal studies would allow for a better understanding of the evolution of compulsive buying,
while expanding the scope of the samples would ensure the representativeness of the findings. On
the other hand, there is a lack of research on environmental factors related to CB. Recent studies
suggest that certain cultural elements may act as risk or protective factors (Grougiou et al., 2015;
Wang & Zhai, 2022). In Greece, for instance, parenting linked to extended family structures may
prevent CB (Grougiou et al., 2015).
Exploring these effects in other countries would be valuable. Lastly, it is necessary to examine other
variables in the search for a risk profile. Additionally, there is limited data on the prevalence of CB in
different regions, making it difficult to represent some areas and compare countries.
Finally, it is essential to explore other variables in the search for a risk profile. Although this study has
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analysed sex and age, it is important to investigate whether there are common patterns in socio-
economic or educational terms. The socio-economic circumstances of each country, in relation to the
economic crises that may have influenced them, as well as the COVID-19 period, could be confounding
factors that may modulate or mediate CB.
CONCLUSIONS
This study estimates that the prevalence of compulsive buying behaviour ranges from 1.8% to 12.41%
in the general population, with differences between countries and higher levels in Asia and America.
CB primarily affects young women, suggesting vulnerability related to the search for social approval
and the use of buying as an escape from self-criticism, particularly in individuals with low self-esteem
or insecure attachment. The comorbidity of CB includes substance abuse, problematic internet use,
and workaholism, as well as personality disorders and low self-esteem. Protective factors such as life
satisfaction, self-regulation, and financial knowledge can reduce the risk of CB. Although cultural
differences influence buying behaviour, the evidence found in this study suggests the importance of
incorporating financial management skills into preventive strategies that promote reflective and
responsible consumption habits, such as planning and budgeting, as well as components like self-
efficacy, self-regulation, intrinsic and prosocial goals, and active coping strategies. Additionally,
strengthening the support network of individuals affected by CB should be emphasised.
DATA AND MATERIALS AVAILABILITY
Data and/or materials from the study are available upon request to the corresponding author.
DECLARATION OF GENERATIVE AI AND AI-ASSISTED TECHNOLOGIES IN THE WRITING PROCESS
The authors declare that no AI or AI-assisted technology was used in the writing process of the article.
CONFLICT OF INTEREST
The authors of this article declare no conflict of interest.
FUNDING
This study received no funding.
AUTHOR CONTRIBUTIONS
Idea: JS-G; GMC; VJV-B; Literature review (state of the art): JS-G; GMC; Methodology: JS-G; GMC; Data
analysis: JS-G; GMC; Resultados: JS-G; GMC; VJV-B; Discussion and conclusions: JS-G; GMC; AV-M; VJV-