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Research Article

Towards a conceptual framework for the prevention of gambling-related harms: Findings from a scoping review

Contributed equally to this work with: Jamie Wheaton, Ben Ford

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Writing – original draft

* E-mail: [email protected]

Affiliation School of Geographical Sciences, University of Bristol, Bristol, United Kingdom

Affiliations School of Geographical Sciences, University of Bristol, Bristol, United Kingdom, Psychological Sciences, School of Natural and Social Sciences, University of Gloucestershire, Cheltenham, United Kingdom, The Department of Psychology, Edge Hill University, Ormskirk, United Kingdom

Roles Conceptualization, Funding acquisition, Supervision, Writing – review & editing

¶ ‡ AN and SC also contributed equally to this work.

Affiliation University of Bristol Business School, University of Bristol, Bristol, United Kingdom

• Jamie Wheaton, 
• Ben Ford, 
• Agnes Nairn, 
• Sharon Collard

• Published: March 22, 2024
• https://doi.org/10.1371/journal.pone.0298005
• Peer Review
• Reader Comments

The global gambling sector has grown significantly over recent years due to liberal deregulation and digital transformation. Likewise, concerns around gambling-related harms—experienced by individuals, their families, their local communities or societies—have also developed, with growing calls that they should be addressed by a public health approach. A public health approach towards gambling-related harms requires a multifaceted strategy, comprising initiatives promoting health protection, harm minimization and health surveillance across different strata of society. However, there is little research exploring how a public health approach to gambling-related harms can learn from similar approaches to other potentially harmful but legal sectors such as the alcohol sector, the tobacco sector, and the high in fat, salt and sugar product sector. Therefore, this paper presents a conceptual framework that was developed following a scoping review of public health approaches towards the above sectors. Specifically, we synthesize strategies from each sector to develop an overarching set of public health goals and strategies which—when interlinked and incorporated with a socio-ecological model—can be deployed by a range of stakeholders, including academics and treatment providers, to minimise gambling-related harms. We demonstrate the significance of the conceptual framework by highlighting its use in mapping initiatives as well as unifying stakeholders towards the minimization of gambling-related harms, and the protection of communities and societies alike.

Citation: Wheaton J, Ford B, Nairn A, Collard S (2024) Towards a conceptual framework for the prevention of gambling-related harms: Findings from a scoping review. PLoS ONE 19(3): e0298005. https://doi.org/10.1371/journal.pone.0298005

Editor: Francis Xavier Kasujja, Medical Research Council / Uganda Virus Research Institute & London School of Hygiene and Tropical Medicine Uganda Research Unit, UGANDA

Received: July 14, 2023; Accepted: January 16, 2024; Published: March 22, 2024

Copyright: © 2024 Wheaton et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All files are available at the Open Science Framework: https://osf.io/d7js2/ .

Funding: JW, BF, AN, and SC carried out this work as part of the Bristol Hub for Gambling Harms Research, which is funded by a grant from the national charity GambleAware. GambleAware is funded by voluntary donations from the gambling industry. Governance procedures and due diligence provide safeguards to ensure the Hub’s independence from GambleAware and the gambling industry. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. https://www.begambleaware.org/about-us .

Competing interests: All the co-authors (JW, BF, AN and SC) have received funding from GambleAware through their work at the Bristol Hub for Gambling Harms Research. The Bristol Hub for Gambling Harms Research (2022-2027) is funded by GambleAware which is funded by voluntary donations from the gambling industry to build capacity in interdisciplinary gambling harms research. Governance procedures and due diligence provide safeguards to ensure the Hub’s independence from GambleAware and the gambling industry. Neither GambleAware nor the gambling industry have any input to the strategic, operational or research activities of the Hub. SC has also received research funding from the Gambling Commission Regulatory Settlement funds. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Introduction

The gambling sector has seen significant growth in recent years due to liberal deregulation and digital transformation [ 1 ]. As of 2021, the global online gambling industry alone was worth US$61.5 billion, forecast to rise to US$114.4 billion by 2028 [ 2 ]. The increasing accessibility of gambling—such as the products available through smartphones [ 3 ]—increases the possibility of gambling-related harms (GRH) [ 4 ]. These harms are wide-ranging, covering numerous dimensions (such as financial, emotional or cultural) and they are not restricted to the gambler, also affecting their families and social networks [ 5 ]. There is therefore a growing support for a public health (PH) approach to GRH [ 4 , 6 – 9 ]. Thomas et al. [ 7 ] highlight five key pillars of a PH approach to GRH [ 7 ]: the development and implementation of a comprehensive public health framework to prevent gambling harm; the elimination of industry influence from research policy and practice; the addressing of structural characteristics which impede gambling harm prevention; strong restrictions on gambling-related marketing; and an independent public health-based education programme. Additionally, Price et al. [ 8 ] argue that operationalising a public health approach to gambling harms also requires five strategies: health promotion; health protection; disease prevention and harm minimisation; population health assessment; and health surveillance.

As the above examples highlight, a PH approach requires a multifaceted response with a range of initiatives and interventions not only to treat GRH on presentation, but also to prevent them from occurring in the first instance. Recent reports have identified that the targets and strategies of PH approaches to GRH must: recognise that the input of those with lived experience is integral [ 10 ]; understand product-based risks [ 11 – 13 ]; and include targeted advocacy and campaigning [ 14 , 15 ]. Other authors have suggested that initiatives should target affected others, wider communities [ 16 ] and entire populations [ 9 , 17 ] whilst also targeting specific at-risk communities [ 18 – 20 ].

To minimise GRH most effectively, frameworks which identify problems and present potential mitigating strategies are necessary [ 7 ]. A range of frameworks already exist, but they vary in their content and intent. These frameworks are introduced in Table 1 . Some frameworks have applied a pre-existing theoretical or methodological lens to describe gambling behaviour in relation to underpinning mechanisms or explanatory factors [ 21 – 25 ]. Several have focused predominantly on the conceptualization of harm and subsequent harm minimization strategies [ 17 , 26 – 29 ] with others focusing almost exclusively on strategies to minimise harm [ 30 , 31 ]. These harms-focused frameworks generally agree upon the types of harms experienced, even if demarcations between categories vary. Finally, several harms frameworks recognise the importance of targeting different sections of society [ 5 , 9 , 17 , 26 ].

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https://doi.org/10.1371/journal.pone.0298005.t001

There are, however, two themes which appear consistently throughout the frameworks in Table 1 : (1) a socio-ecological approach which recognises the need to focus on the relationship between harms and individual, community and societal determinants, and (2) the use of an established harms framework that is specific to the complex impacts of gambling [ 9 ]. Far less prominent are the range of goals and strategies that might make up a ‘public health’ approach to GRH. Thus, the construction of a comprehensive conceptual framework which can unify stakeholders towards GRH requires an exploration of the breadth of possible PH goals and strategies. Additionally, a comprehensive conceptual framework should be nuanced for the different strata of society which may experience GRH [ 7 ].

Our proposed conceptual model for GRH as a PH issue can help to overcome two significant barriers. First, the absence of a synthesizing framework makes mapping and evaluating the discrete aims of cross-disciplinary research or applied settings difficult. Secondly, no tool exists for organizations to evaluate current resource and service allocation. A PH approach requires rigorous and high-quality research evidence to inform decision-making [ 32 – 36 ]. Tools to support this understanding will help to identify opportunities for new organizations or future initiatives [ 37 , 38 ]. A shared or common conceptual framework would facilitate knowledge transfer between key stakeholders within different disciplines. Therefore, a shared framework would support the mapping of key research and initiatives in the gambling landscape and make communication and coordination easier amongst a variety of stakeholders.

Furthermore, a conceptual PH framework for GRH could benefit from lessons learned in other commercial, legal but potentially harmful, sectors, such as the alcohol, tobacco, and products high in fat, salt and sugar (HFSS) sectors. Although these comparisons are not widely prevalent within the frameworks highlighted in Table 1 , previous research has explored how specific interventions or approaches within other sectors can inspire similar strategies towards GRH. Friend and Ladd [ 39 ] evaluate how a PH approach to GRH could learn from interventions to curb tobacco advertising. Thomas et al. [ 40 ] explore the opinions of PH experts within these industries and find that industry actors provide a barrier to the instigation of PH policies through political lobbying and donations, thus highlighting the need for a delineation between policymakers and industry. Other work highlights how industry actors use messages around the complexities involved with deploying a PH approach to deter such an approach being taken [ 41 ]. A successful PH approach to GRH—with inspiration from other sectors—should be informed by approaches in those sectors which are successful in reducing harms. Accordingly, the aim of this paper is to explore how PH approaches to GRH could learn from the tobacco, alcohol and HFSS sectors. We propose a conceptual framework which aligns disparate PH strategies and approaches—and potentially unites sector stakeholders—towards the prevention of gambling-related harms. Moving beyond the frameworks we have explored above, our framework was constructed first of all by carrying out a scoping review of extant PH approaches towards alcohol-, HFSS-, tobacco-, and gambling-related harms. This explored—and developed a categorisation of—PH approaches found within each sector. Approaches to crime were also part of the original research focus but were excluded as we are concentrating only on legal products. We then categorised the PH approaches found during our scoping review and intersected them with the socio-ecological gambling-harms model proposed by Wardle et al. [ 17 ], allowing conceptual relationships to be drawn between potential PH strategies and goals, different levels of society, and different forms of GRH.

This paper is structured as follows. Firstly, we outline the methodology of the scoping review of previous PH approaches towards alcohol-, tobacco-, HFSS-, and gambling-related harms. Secondly, we evaluate the findings of the review, derived from a narrative analysis of the strategies prevalent within the sample of literature. Thirdly, the strategies and goals which emerged from our scoping review are developed into a conceptual framework for GRH. This also incorporates a socio-ecological model and a categorization of GRH outcomes by type of harm as well as severity and temporal experience of harm. Finally, we highlight how the framework can be used by a range of stakeholders towards the development of interventions which minimise GRH.

We conducted a scoping review to explore existing PH approaches to GRH, and how they could learn from the tobacco, alcohol and HFSS sectors. Our initial focus also included crime-related harms, given their impact on society, communities and individuals alike. However, the decision was made during the initial search to concentrate only on legal products. This was because the significantly different regulatory and economic relationship that industries supporting crime have with society and government given their illegality meant this topic was considered the ‘least’ relevant. We followed PRISMA guidelines [ 63 ] for the identification, screening, eligibility and inclusion of papers, detailed in Fig 1 . We preregistered the scoping review on the Open Science Framework (OSF) ( https://osf.io/d7js2 ), and broadly followed the five stages as recommended by Arksey and O’Malley [ 64 ]. Our first stage consisted of the identification of the scoping review’s guiding research question: what is a public health approach to GRH and how can it learn from other sectors? To answer this question, our aim was to identify existing PH approaches towards tobacco-related, HFSS-related, alcohol-related harms alongside those towards GRH. These approaches would then be synthesized to develop a categorisation of PH approaches to GRH that would be developed into a collaborative framework.

https://doi.org/10.1371/journal.pone.0298005.g001

The second stage was the identification of relevant studies. We (BF; JW) began this process with an initial search in November 2022, with search terms (public health) AND (approach OR framework OR tackling)) AND (gambl* OR tobacco OR alcohol OR crime OR fast food) entered into the Web of Science, PsycInfo, Scopus, Ovid Medline and Ovid Embase databases. We first selected ‘fast food’ as a search term but our initial search omitted research focused on other HFSS products such as sweet, salty and high fat food and beverages. We therefore conducted an additional search in January 2023 for papers related to HFSS products, using search terms ((public health) AND (approach OR framework OR tackling)) AND (food AND fast OR processed OR (high AND sugar OR salt OR fat) into the aforementioned databases. Eligible papers were those which adopted a specific PH focus towards the respective harms of each sector. The other inclusion criteria stipulated a focus on highly developed economies and articles written in English. With the research conducted within Great Britain, we sought papers published after 2005, the year in which the Gambling Act—which controls gambling in Great Britain—was given royal assent and which transformed gambling in Britain into a heavily advertised, deregulated commercial activity [ 1 ]. Once records had been identified from academic databases, we downloaded citation files and added them to Mendeley. We removed duplicates and then imported the remaining citation information into Excel. Additionally, we searched relevant websites for grey literature (see S1 Appendix ), using the search terms “public health approach” or “public health framework” or “public health”. Grey literature was sought from relevant organisations to provide insight beyond peer-reviewed journals. We downloaded relevant grey literature full texts and stored them in an online folder. Citation information was added into the Excel file by hand. We used the grey literature to compare approaches explored in peer-reviewed journals with those highlighted by non-academic organisations such as Alcohol Focus Scotland [ 65 ], or the Gambling-Related Harm All Party Parliamentary Group [ 66 ]. However, given the difference in robustness between peer-reviewed and grey literature, our findings are based solely upon those found within peer-reviewed literature.

Our initial searches returned a working sample of 15,378 titles after deduplication. We (JW and BF) then sifted through the working sample (N = 15,378) according to the inclusion criteria by title, abstract and full text. Our first sift by title saw the working sample reduced, according to the aforementioned criteria, to a new working sample of 1,037. At this point, we retained and separated alcohol-, HFSS- and tobacco-related review papers and gambling-related research papers into a new Excel sheet. We prioritised review papers for alcohol-, HFSS-, and tobacco-related harms (rather than papers describing individual studies) due to the short timeframe of the scoping review, and the inclusive nature of these reviews. Our second sift across both searches involved screening abstracts against the inclusion criteria and resulted in retaining 255 papers. Full details of excluded papers—and the reasons for exclusion—can be accessed through the OSF link. We (JW and BF) screened 231 alcohol-, tobacco- or HFSS- review papers by title, abstract and full-text, resulting in 43 retained for the fourth stage of data charting. One example of inclusion was Crombie et al.’s [ 67 ] review of interventions designed to prevent or reduce alcohol-related harms. Published after 2005, their review highlighted the range of interventions deployed in a range of advanced economies which can be adopted as a PH approach.

We reviewed individual gambling articles as per the original protocol, seeking empirical studies which either sought to explore the implementation of a public health strategy, or explored the requirements for such a strategy with findings drawn from participants’ behaviours or against the background of wider socio-economic, or commercial determinants. We also retained conceptual articles which we felt provided context to the strategies found within empirical articles. We screened 24 gambling-focused articles by full-text (two additional articles were identified from full-text reading), resulting in 20 being retained for data charting. An example of a gambling-related paper that fulfilled the inclusion criteria was Kolandai-Matchett et al.’s [ 33 ] study, which explored the implementation of a PH approach towards GRH in New Zealand.

Following Arksey and O’Malley [ 64 ], the final stage of our scoping review was the narrative analysis of themes prevalent within the sample of literature. We (JW and BF) analysed the specific PH-related approaches within each individual study or review paper. The full categories of data we extracted are introduced in Table 2 . We focused on the data extracted under the category of ‘Summary of Findings and Themes of Public Health’. Our narrative analysis grouped PH approaches by coding strategies and interventions according to type, thus developing a broader categorization of strategies which can be applied to reduction or prevention of GRH. We also coded the end goals or aims of strategies, thus resulting in broad PH goals which, when achieved, may result in the prevention or reduction GRH. We (JW and BF) coded strategies and goals separately, but found upon comparison that our respective analyses of goals and strategies returned similar results. After negotiation over the definition and categorization of goals and strategies, we ended with three broad PH goals which could be achieved by three broad PH strategies. We define these PH goals and strategies in the results of the narrative analysis section that follows below. These goals and strategies apply across tobacco, alcohol, HSSF and gambling.

https://doi.org/10.1371/journal.pone.0298005.t002

Results of narrative analysis

The sample of alcohol-, HFSS-, and tobacco-focused reviews and gambling-focused studies (N = 63) is introduced within Table 3 , alongside the interventions explored within each paper. Table 3 highlights the wide range of journals and jurisdictions present within the sample. Whilst the interventions and approaches varied, our analysis of data from the scoping review found that interventions were driven towards achieving three broad PH goals and three broad PH strategies. These are discussed in the following two sections.

https://doi.org/10.1371/journal.pone.0298005.t003

Three broad public health goals across potentially harmful product sectors

The PH Goals identified were (1) the prevention of harms, (2) the regulation of industry, and (3) support for those experiencing harms. This reflects the need to include both health promotion and an understanding of the epidemiology of non-communicable diseases. We discuss these three goals below.

We define the goal of prevention of harms as the need to prevent harms from occurring at the earliest opportunity through societal level awareness and destigmatisation . We highlighted prevention-focused strategies as occurring through health promotion campaigns which sought to denormalise harmful behaviours, whether through ‘responsible drinking’ campaigns [ 67 , 68 ], the provision of healthier alternatives to HFSS products [ 69 , 70 ] or the delivery of school-based awareness campaigns to reduce tobacco consumption [ 71 , 72 ]. Also prevalent across the reviews into alcohol-, HFSS-, and tobacco-related harms, was evidence of mass-media or educational campaigns which generate societal-level awareness. Our analysis, however, highlighted a lack of evidence of strategies which seek to destigmatise GRH. There is a perceived need according to stakeholders to increase public awareness of GRH [ 33 , 73 ], as well as to understand how different contexts may lead to GRH [ 74 – 76 ].

We define the goal of the regulation of industry as the need to prevent harms through the central management of industries and their products . How regulation should occur differed within the sample depending on the harm under study. Themes common across the alcohol-, HFSS-, and tobacco-focused reviews included the management of political lobbying [ 77 ], and the taxation of products [ 70 , 78 – 85 ]. As with the goal of prevention of harms, studies into the prevention or reduction of GRH identified a need for stronger regulation of the gambling industry, as opposed to offering evidence of the efficacy of legislative or regulatory measures already in place. Studies of stakeholders within the industry highlight the need to manage industry involvement within political lobbying processes [ 74 ], and the need to regulate specific products which are perceived as harmful [ 74 , 75 ]. Regulatory measures towards the prevention of GRH could therefore learn from approaches to harms within other sectors. Whilst the PH goal of regulation was prevalent across all sectors, only studies into GRH were unable to provide any evidence of the efficacy of regulatory measures.

We define the overarching goal of support of those experiencing harm as the need to treat harms already experienced through targeted , specialist help . This theme of targeted support was prevalent across every sector, although the strongest evidence linking targeted support to the reduction of harms was found within tobacco-related reviews [ 70 , 82 , 86 , 87 ]. There was also evidence within the other sectors of targeted support incorporating family or affected others [ 88 ] or community involvement [ 89 ]. Data from our scoping review again found that evidence bases within the other sectors were more developed in relation to support, than within the gambling sector. In contrast to the other sectors under study, studies into GRH provided evidence from stakeholders on how fulfilling the goal of support could lead to the reduction of harms [ 33 , 73 , 74 , 90 , 91 ]. However, the evidence linking targeted support to GRH still highlights the need to upscale support-led initiatives towards the reduction of societal-level harms [ 91 ].

Three broad public health strategies across potentially harmful product sectors

In addition to delineating three common public health goals across harmful product sectors, our narrative analysis of scoping review data also identified three broad types of public health strategy that can be used to achieve these goals: (1) education and awareness (EA), (2) screening, measurement and intervention (SMI) and (3) understanding environment and product (UEP).

The strategy of ‘education and awareness’ (EA) comprises initiatives which seek to prevent or reduce harms through the provision of research-led information . Within the alcohol sector, EA initiatives were aimed at children and adolescents [ 92 , 93 ], whilst also seeking to encourage ‘responsible drinking’ [ 67 , 68 ]. Within HFSS-related reviews, EA strategies were found within the provision of calorie or nutrition information [ 94 , 95 ], access to healthier alternatives [ 69 ], and the use of widespread awareness campaigns [ 78 , 96 – 98 ]. Widespread awareness campaigns on the harms of tobacco consumption were analysed as effective [ 71 , 72 , 82 , 99 ], particularly when used to encourage changes in harmful behaviours at a young age [ 100 ]. EA strategies identified within gambling-related studies highlighted a need for initiatives and evidence towards the destigmatisation of GRH through their recognition as a PH issue [ 33 ], and increased awareness of GRH amongst healthcare and service providers [ 73 ]. Research also concurred that EA-led, mass awareness should be disseminated free from commercial discourse such as ‘responsible gambling’ which enables the industry’s avoidance of responsibility and promotes the continuation of gambling regardless of level of harms experienced [ 75 ].

Secondly, Screening, Measurement, and Intervention (SMI) strategies are concerned with the screening of harms , the subsequent intervention where required , and the measurement required to track the prevalence of harms . SMI strategies—specifically intervention strategies—also comprise the management of industry practices, and their involvement within policymaking. Within alcohol-related reviews, interventions were geared towards the prevention of drink-driving [ 67 , 101 ], as well as support for those who are already experiencing alcohol-related harms [ 67 , 88 , 92 ]. Within HFSS-focused reviews, SMI strategies consisted of the management of industry involvement within policymaking [ 77 ] and targeted dietary interventions [ 96 ]. SMI strategies explored within tobacco-focused reviews included targeted, cessation interventions [ 82 , 86 , 88 ], advice given to patients and the training of staff [ 70 , 87 ], and the management of industry practice [ 77 ]. SMI strategies were also explored within tobacco-focused reviews through the adoption of the World Health Organisation’s Framework Convention on Tobacco Control (FCTC) [ 84 ]. Within gambling-related studies, SMI strategies were again found, to provide targeted support to patients—and affected others [ 91 ]—through the need for more widespread support services [ 33 , 73 ] whilst also being linked with treatment for other comorbidities. Targeted, specialist support would also be aided by the deployment of an easily accessible screening tool [ 73 , 90 ]. The evidence shows strong support for the clear separation of the gambling industry from the government, as well as the transparency of lobbying by the industry itself [ 74 ]; and conceptual papers agree that regulation and policy should be immune to industry influence [ 32 , 102 , 103 ]. The separation of government and industry influence could also include the deployment of self-exclusion schemes which allow individuals to exclude themselves from gambling to avoid harms. Evidence presented by Kraus et al. [ 104 ] suggests that the state and the industry alike are ineffective at enforcing self-exclusion registers which can be easily circumvented. On the other hand, the authors still conclude that state-regulated registers are more likely to be effective at maintaining self-exclusion compared to those maintained by operators whose primary focus is revenue over harm limitation.

Strategies relating to the understanding of environment and product (UEP) explore how harmful behaviours emerge within specific contexts or from specific products , in order to prevent or reduce further harms . UEP strategies therefore seek to understand the social and environmental contexts which may lead to GRH and how these might be addressed. Alcohol-focused reviews provided three specific UEP strategies, namely reduced hours of sale [ 67 , 105 ], reduced outlet density [ 67 ], and the adoption of tax and price controls to deter harmful consumption [ 106 ]. HFSS-focused reviews also explored the effect of taxes on the consumption of unhealthy products [ 79 , 80 ], the effective protection of children from marketing [ 70 , 78 , 96 , 97 , 107 ], the display of calorie information on menus [ 94 , 95 ], and the impact of traffic light system labelling on the consumption of sugar-sweetened beverages [ 81 ]. Tobacco-focused reviews highlighted the efficacy of tax and price controls alongside the deployment of smoke-free spaces which denormalises smoking [ 82 , 99 ]. As with the other two categories of PH strategy, gambling-focused studies highlighted a need for further UEP strategies to reduce harm, including more effective regulation of specific products—such as EGMs which were highlighted within the sample of studies as more harmful—which may encourage initial and continued harmful gambling behaviours [ 74 , 75 ], as well as the social cues which may encourage a pathway to harmful gambling behaviours [ 76 ]. Indeed, interactions with specific products may also be intertwined with various social interactions with friends [ 76 ], or with staff [ 74 ], and a deeper understanding of how GRH may arise from specific environments would allow best practice (from stakeholders) or regulation to protect those at risk.

Conceptual papers

Aside from the PH goals and strategies highlighted above, our scoping review also uncovered peer-reviewed papers whose approach was conceptual in nature. Conceptual approaches were mainly prevalent within papers whose focus was on the reduction or prevention of GRH. For the purposes of this paper, our grouping of conceptual papers included those which developed frameworks [ 36 , 49 ], highlighted the necessary collaborations for a PH approach [ 35 ], or viewpoints which underlined the need for clear delineation between industry and policymaking bodies [ 103 ]. These papers were retained within the initial sample thanks to their valuable insight.

Identifying existing frameworks

The categorisation of PH approaches that emerged from our scoping review was then used to develop a collaborative framework to address GRH. Our scoping review sought to identify literature that made recommendations for PH approaches or acknowledged wider socio-economic determinants of health in the gambling, tobacco, alcohol, and HFSS sectors. In many cases, the impact of PH interventions was not targeted at specific harms and approaches were intentionally operationalised to have general far-reaching impact. However, we recognised following our review that a framework that relates specific GRH to approaches requires categories of harms to be discernible. We also aimed to ensure that PH strategies identified from other areas were made relevant to gambling. It was thus necessary to include frameworks which have conceptualised GRH as this was not apparent in the articles identified from the scoping review. Therefore, separately from the scoping review but using the same databases, we identified a number of existing gambling-harm frameworks using the terms “gambl* AND harm AND (framework OR concept* OR strategy)”. We then evaluated the commonalities and differences and made a pragmatic decision about which conceptualisation to incorporate into our framework.

The PH goals and strategies outlined above constitute the core of our conceptual framework. However, such strategies and goals often only target specific levels of society [ 9 , 17 , 27 , 29 , 124 , 125 ] and individual categories of GRH [ 5 , 26 ]. There are also considerable differences between gambling and the other sectors reviewed. Approaches to alcohol-, HFSS-, and tobacco-related harms generally focused on the amendment of products such as the reduction of fat content in HFSS foods, the regulation of marketing, education to encourage behaviour change, and taxation. GRH-related papers, on the other hand, highlighted the need to counter industry interests, the need for more effective awareness of—and screening for—GRH within healthcare systems, and developing societal-level messaging that destigmatises GRH. Additionally, digitalisation affects gambling activities in a way that does not apply to alcohol, tobacco or HFSS consumption [ 1 , 3 ]. Therefore, we do not wish to suggest that the PH approaches evident in the scoping review represent an exhaustive list, nor that all approaches in other sectors are necessarily effective for, or relevant to, gambling. Rather, by grouping the existing approaches into broad overarching categories, we hope the conceptual framework is able to categorise the variety of approaches currently in-focus, as well as those yet to be proposed. Furthermore, for those wanting specific strategy proposals, researchers have made attempts to list numerous potential strategies to tackle GRH from a PH approach [ 7 , 8 ]. Our proposed conceptual framework is different because it offers a way to incorporate, and systematically organise, multiple PH goals and strategies while also taking into account the different social strata described in socio-ecological models and focusing on multiple categories of GRH.

In this section, we describe the process we followed to produce our conceptual framework. Firstly, we incorporate the socio-ecological model with the PH goals and strategies identified from the scoping review. Secondly, we then incorporate different GRH-related outcome categories, whilst also developing the framework to measure the severity and timescale over which GRH may be experienced. Following this process ensured that our framework was fully developed from the identification of PH goals and strategies, towards their application at different levels of society, and the prevention of GRH which may be wide-ranging and complex in nature.

Incorporating the socio-ecological model of gambling

Previous discussions of PH approaches highlight the necessity for a framework to understand gambling impacts at various levels of society [ 9 , 17 , 27 , 29 , 124 , 125 ]. The socio-ecological model is appropriate given its links to other PH domains [ 125 – 128 ] and its acknowledgement of the social and environmental determinants encountered by individuals which may also lead to GRH or shape people’s experiences of them [ 125 ]. The socio-ecological model emphasises that situational factors interact with an individual’s biopsychological characteristics [ 17 ]. For a long time, the focus of GRH research has been an individualised approach. We agree with others [ 7 , 8 ] who argue that focus should be shifted to wider determinants of GRH. Our model draws on similar strata highlighted by Wardle et al. [ 17 ], moving from ‘Individual’ to ‘Interpersonal’ to ‘Community’ and ‘Societal’, as introduced in Fig 2 . This stratification allows the targeting of intervention and treatment at specific parts of the population [ 9 , 17 , 29 ], and the understanding and mapping of the relations between various approaches or outcome goals. Public Health England (PHE) [ 129 ] used the socio-ecological model for a gap analysis of gambling risk factors, thus demonstrating the utility of the model as a tool to map research. For gambling specifically, the model shifts the focus from individual treatment approaches and explicitly recognises the harm caused to affected others [ 91 ], social groups [ 76 ], and at a societal level [ 9 ].

https://doi.org/10.1371/journal.pone.0298005.g002

The ‘Individual’ level focuses on biopsychological characteristics that might be classified as ‘risk-factors’ as well as issues such as individualised interventions. The ‘Interpersonal’ level focuses attention on the social and family structures of at-risk individuals, such as the partners of pathological gamblers or interpersonal interventions. The ‘Community’ level relates to local or online environments, community groups, alongside institutions such as schools, banks and workplaces. Crucially, the ‘Community’ level targets ‘not-at-risk’ groups as well as identified ‘at-risk’ groups. Finally, the ‘Societal’ level encompasses whole-population approaches such as public policy decisions, national campaigns, and macro-structures that determine legal and cultural practices [ 9 , 17 ].

Connecting the conceptual framework to different categories, severity, and timeframes of harm

Whilst developed against a socio-economic model to explore impacts against individuals, communities and societies, our framework still requires development in its relationship with specific harms. We therefore firstly developed the framework in accordance with categories of harm outcomes that are already well-established within gambling-related research. Secondly, we addressed how specific GRH can be experienced more or less intensely over different periods of time.

Different categories of harm.

There are various conceptualizations of GRH. For example, PHE [ 129 ] classifies harms into financial; relationship; mental and physical health; employment and education; crime and anti-social behaviour; and cultural, with similar categories seen in other works [ 5 , 29 ]. The discrete categories proposed by Langham et al. [ 5 ], PHE [ 129 ], and Marionneau et al. [ 29 ] are useful if GRH is the focus of a framework, although even they rely on further sub-categorisation to encompass specific outcomes. However, in seeking to develop an overarching conceptual framework with a considerable number of intersecting goals, strategies and approaches, we decided to adopt Wardle et al.’s [ 17 ] concise approach which categorises harms into three broad categories: resources , relationships , and health , underneath which sit six sub-categories and fifty indicators. Table 4 maps the more detailed GRH categorisations against Wardle et al.’s [ 17 ] simplified three-category framework. Crime is defined by Wardle et al. [ 17 ] as a resource-based GRH, as emphasis is placed on measuring the impact of gambling behaviour on organisations, systems and victims through the medium of money or resource cost. However, whilst we agree that this is useful for the measurement of harm and for public health decision making [ 130 ], it is important to note that the relationship between gambling and crime is itself multifaceted and complex [ 131 ] and, as it is not a legal activity, it was excluded as an area of focus from the scoping review. Crime that results from gambling, however, is still categorised as a GRH. Crimes that result from gambling may consist of anti-social behaviour, entail gambling as a contributory factor [ 17 ], or systemic crimes where regulations are not followed by operators (for example, non-compliance in relation to unfair practices, underage gambling, or unfair advertising) [ 131 ]. All categories of harm—resources, relationships, and health—may arise as outcomes from gambling behaviour, as well as forming determinants which influence gambling behaviour itself.

https://doi.org/10.1371/journal.pone.0298005.t004

Different severities and timescales.

Existing harms frameworks acknowledge that GRHs exist on a temporal continuum [ 5 , 27 ] from brief (or episodic) to long-term, or even intergenerational. Additionally, the harms experienced from gambling at any level of society can range from inconsequential, to general or crisis-level [ 5 ]. As before, we have adapted Wardle et al.’s [ 17 ] categorization of harms within our conceptual framework for measurement across this continuum, introduced in Fig 3 . These dimensions are a vitally important consideration when evaluating the implications or impact of gambling behaviour on individuals, social groups, communities, and societies in order to inform decision-making.

https://doi.org/10.1371/journal.pone.0298005.g003

PH approaches encompass a range of goals and strategies, allowing for an understanding of complexity and nuance of societal health issues whilst retaining synergism. The core PH goals and strategies which emerged from our scoping review form the central interacting strands of a comprehensive conceptual framework for the prevention of GRH. This conceptual framework could also be helpful given that our analysis has demonstrated that the evidence base of a PH approach to GRH may be behind the curve compared to other sectors. To cement the shift away from focusing on individuals and reflect that public health approaches aim to minimise harm across families, social groups, communities and whole populations, our conceptual framework nests the PH goals and strategies within the socio-ecological model. To make it comprehensive, the conceptual framework also incorporates different categories of GRH as proposed by Wardle et al. [ 17 ], along with different severities and time scales.

Our full conceptual framework is introduced in Fig 4 . By mapping how PH goals and strategies intersect with gambling harms and how the intersections can be stratified by the socio-ecological model, we propose a highly interactive framework that can be used in research, policy and practice. The three broad PH strategies intersect with the three PH goals. At each intersection, the model recognises their relationship to gambling outcomes: either resource-based, relationship-based, or health-based harms. Importantly, each strategy-goal intersection and related GRH can be differentiated by level of socio-ecological model, spanning from individual to family and social networks , community , and society . Whilst many reports focus on ‘selective’ or ‘at-risk groups’ in the community [ 9 ], our framework additionally focuses on ‘untargeted’ approaches, given the importance of preventative measures in public health approaches at a societal level.

https://doi.org/10.1371/journal.pone.0298005.g004

At a conceptual level, the tool allows researchers from varied disciplines to understand how their work intersects with society, specific GRH, and the wider gambling field. The framework will support the understanding of project related implications and impact. Furthermore, for stakeholders wishing to understand how a PH approach to gambling can be delivered or the types of considerations that need to be addressed, this conceptual mapping should prove useful. However, the framework’s utility is most evident through its use as a tool for applied and research settings. The framework can be used for organising, evaluating, and strategising in a research or service setting, with the subsequent benefit of facilitating communication and coordinated effort.

By populating the various intersections of the framework, stakeholders can systematically map research, policies, or services. The framework therefore serves four important functions, introduced in Fig 5 . Firstly, the framework facilitates mapping of the breadth and depth of initiatives designed to prevent or reduce GRH, and, in doing so, identifies gaps in research or provision. Secondly, the framework enables the evaluation of interventions and approaches in relation to different levels of society or against the different PH goals or strategies. Thirdly, the framework enables stakeholders to identify relationships between different intersections of the framework, service provisions, research areas within or across disciplines, or the aims of research and applied settings. Finally, the framework facilitates the understanding relationships and establishes potential avenues for communication and cooperation across different stakeholder groups or disciplines. These functions are introduced in further detail below.

https://doi.org/10.1371/journal.pone.0298005.g005

The conceptual framework as a mapping tool

The primary function of the framework is that of mapping . Stakeholders could map support services at the intersection of SMI strategies towards the overarching goal of support at various levels of the socio-ecological model, in relation to specific GRH. Stakeholders could use the framework to understand how provision varies across society. For example, the framework may highlight a lack of services focused at the ‘families and social network’ level, suggesting a need for greater provision for affected others. Additionally, stakeholders may find that services are focusing predominantly on ’health’-related harms such as psychological distress whilst the mapping highlights that greater focus should be placed on reducing financial harms. In this sense, interacting with the framework can provide information to support informed resource allocation and decision-making. Mapping could be done using a matrix, as demonstrated in Table 5 below, which demonstrates the interaction between socio-ecological model, PH goals and strategies, as well as gambling-related outcomes, including ‘non-specific’ outcomes identified by stakeholders outside of the broad categorization offered by Wardle et al. [ 17 ].

https://doi.org/10.1371/journal.pone.0298005.t005

The conceptual framework as an evaluation tool

A further, subsequent function for the conceptual framework is that of evaluation . Researchers could use the framework in a similar way to evaluate published literature. For example, a researcher may wish to evaluate the strength of evidence for awareness campaigns focused on safer gambling practices at different levels of society. This evaluation could focus on the intersections of EA strategies and the goal of prevention, as well as synthesise and evaluate the strength of evidence for other targeted, community, and whole population campaigns. This could then be easily translated into future campaign research or resource allocation in applied settings.

The conceptual framework as a means to identify relationships

The third function is identifying relationships . The relationships between the aims and implications of research can be easily translated to the goals of applied settings, if the framework is shared by research and applied settings. For example, researchers might compile evidence which supports more restrictive regulation of advertising, and therefore find their work situated at the intersection of UEP strategies and the goal of regulation of industry. On the other hand, they might be unsure of the number of organizations who could advocate and raise awareness for their proposals. The mapping of campaign and advocacy groups at such intersections of the framework could therefore identify potential relationships between research and applied efforts and bolster their impact.

The conceptual framework as a facilitator of communication and cooperation

Finally, the framework also facilitates communication and cooperation . Collating research and services on a common landscape highlights relationships between different areas of work and has the potential to cultivate productive interaction between stakeholders. A common framework facilitates a common language to discuss specific issues or initiatives. Furthermore, top-down organization can be facilitated through the mapping of provision to support the coordination of multiple stakeholder groups. For example, an organization focused on treatment and support for a specific group of individuals may wish to partner with other services to increase awareness of service availability. Having an easy-to-understand map of relevant organizations that offer similar provision in different sub-populations or regions, or a map of organizations whose focus is specifically education and awareness, could improve the impact of any action undertaken.

This paper has outlined our proposed conceptual framework for the prevention of GRH which was developed from the narrative analysis of findings from our scoping review. Our proposed framework aligns PH goals and strategies with existing harm frameworks and allows for differentiation at various ‘levels’ of society. By combining these features under a single overarching framework, stakeholders across disciplines can use a common language and work within a shared conceptual frame. The framework’s utility is clearest as an applied tool that promotes the mapping of research, provision, or organizational focus. Doing so facilitates evaluative exercises which can identify important gaps in research and provision through an understanding of depth and breadth of coverage, and leading to informed decision-making and resource allocation. Moreover, mapping can identify relationships between work across disciplines and settings, which has potential to facilitate cross-sectoral communication and coordination. This could strengthen collective efforts, lead to the development of opportunities and initiatives, and encourage both research informed practice and stakeholder involvement in research.

There are four main considerations when critically evaluating the outcome of the current paper. Firstly, we acknowledge that other studies or reviews—depending on the sector under focus—may not have been returned under the search terms used within our scoping review, and there thus may be other approaches which have not been explored here. However, given the variety of sectors explored within our scoping review, we contend that the broad categorisations developed as the base of our collaborative framework would allow the inclusion of other approaches as part of any mapping exercises.

This links to the second consideration, which is that the categories that compose each strand of the framework are broad. However, as each strand (socio-ecological model; PH goals; PH strategies; GRH) is synthesised or adopted from current research, it is possible to dissect these components in greater detail by reviewing the appropriate literature. Thus, researchers within specific disciplines may wish to sub-categorise strands relevant to them. However, this broad categorization has been done intentionally and for pragmatic reasons. Our framework is designed to be highly interactable and for use across disciplines, sectors and settings. It is the intention that broad categories will encourage the framework to be a collaborative platform that is inclusive to all. As long as the intersections described in this paper remain at the heart of further refinements, the framework’s utility as a tool for mapping, evaluation, coordination and communication persists. Further sub-categorization within disciplines should only serve to demonstrate the flexibility of the framework and encourage greater and more nuanced understanding. Similarly, for applied settings, whether the full extent of non-academic activities (e.g., those activities of charities, financial institutions, advocacy groups or services) fall within the proposed public health strategies. Therefore, as the framework is intended to support stakeholders to reduce GRH, this initial proposition should act as a starting point for further refinement.

Thirdly, the distinctions between certain intersections of the conceptual framework are not always exclusive. Strategies can benefit more than one level of the social-ecological model, improve outcomes for more than a single harm or benefit, or can be both preventative and supportive from a PH perspective. The framework is not intended to place restrictions on classification and duplicating information across sections should not be seen as an issue. Although, we recommend that when this occurs in relation to mapping GRH, it would be useful to incorporate an understanding of harm taxonomies [ 5 ] when deciding where to best place a specific research paper.

Finally, the framework’s conceptual utility is inherent in its depiction of how the component strands interact, and these relationships can be understood in greater detail as knowledge and research develops. The framework’s use as a mapping tool for research and practice relies on an ongoing and systematic process of populating and updating information at the various intersections. This can be achieved by individual organizations and researchers if they are using the framework for a specific goal. Given that each individual organization or researcher will have a specific focus due to time constraints or speciality, there is also potential benefit from the development of an online application that can serve the whole community. Therefore, a future initiative could include the creation of such an application.

Supporting information

S1 appendix. grey literature..

https://doi.org/10.1371/journal.pone.0298005.s001

S1 Checklist. Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist.

https://doi.org/10.1371/journal.pone.0298005.s002

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Contentious issues and future directions in adolescent gambling research.

1. Introduction: Underage Gambling

2. summary and aims of paper, 2.1. issue 1: elevated prevalence rates in adolescent samples, 2.2. issue 2: limited evidence of harm, 2.3. issue 3: low help-seeking rates, 2.4. issue 4: precursor to adult gambling: longitudinal evidence, 2.5. issue 5: the gateway hypothesis and activity transitions, 2.6. issue 6: sampling and the visibility of adolescent gambling, 3. enhancing adolescent gambling research and reporting, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Share and Cite

Delfabbro, P.; King, D.L. Contentious Issues and Future Directions in Adolescent Gambling Research. Int. J. Environ. Res. Public Health 2021 , 18 , 11482. https://doi.org/10.3390/ijerph182111482

Delfabbro P, King DL. Contentious Issues and Future Directions in Adolescent Gambling Research. International Journal of Environmental Research and Public Health . 2021; 18(21):11482. https://doi.org/10.3390/ijerph182111482

Delfabbro, Paul, and Daniel L. King. 2021. "Contentious Issues and Future Directions in Adolescent Gambling Research" International Journal of Environmental Research and Public Health 18, no. 21: 11482. https://doi.org/10.3390/ijerph182111482

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Gambling disorder-related illegal acts: Regression model of associated factors

Affiliations.

• 1 1 Psychiatry and Addictology Department, Paul Brousse University Hospital of Villejuif, Assistance Publique - Hôpitaux de Paris (APHP) , Paris, France.
• 2 2 Pole of Psychiatry 75G04, Henry Ey Hospital, Centre Hospitalier Sainte-Anne , Paris, France.
• 3 3 Clinical Investigation Unit BALANCED "BehaviorAL AddictioNs and ComplEx mood Disorders", Department of Addictology and Psychiatry, University Hospital of Nantes , Nantes, France.
• 4 4 EA 4275 SPHERE "bioStatistics, Pharmacoepidemiology and Human sciEnces Research tEam", Faculties of Medicine and Pharmaceutical Sciences, University of Nantes , Nantes, France.
• 5 5 EA 4430 CLIPSYD "CLInique PSYchanalyse Développement", University of Paris Ouest Nanterre La Défense , Paris, France.
• 6 6 Louis Mourier Hospital of Colombes, Assistance Publique - Hôpitaux de Paris (APHP) , Paris, France.
• 7 7 Marmottan Medical Center, GPS Perray-Vaucluse , Paris, France.
• 8 8 Department of Adult Psychiatry, Sainte-Marguerite University Hospital of Marseille , Marseille, France.
• 9 9 Psychiatry Laboratory, Sanpsy CNRS USR 3413, University of Bordeaux and Charles Perrens Hospital , Bordeaux, France.
• 10 10 Psychiatry Department, University Hospital of Clermont-Ferrand , Clermont-Ferrand, France.
• 11 11 Unit of Methodology and Biostatistics, University Hospital of Nantes , Nantes, France.
• PMID: 28198636
• PMCID: PMC5572995
• DOI: 10.1556/2006.6.2017.003

Background and aims Gambling disorder-related illegal acts (GDRIA) are often crucial events for gamblers and/or their entourage. This study was designed to determine the predictive factors of GDRIA. Methods Participants were 372 gamblers reporting at least three DSM-IV-TR (American Psychiatric Association, 2000) criteria. They were assessed on the basis of sociodemographic characteristics, gambling-related characteristics, their personality profile, and psychiatric comorbidities. A multiple logistic regression was performed to identify the relevant predictors of GDRIA and their relative contribution to the prediction of the presence of GDRIA. Results Multivariate analysis revealed a higher South Oaks Gambling Scale score, comorbid addictive disorders, and a lower level of income as GDRIA predictors. Discussion and conclusion An original finding of this study was that the comorbid addictive disorder effect might be mediated by a disinhibiting effect of stimulant substances on GDRIA. Further studies are necessary to replicate these results, especially in a longitudinal design, and to explore specific therapeutic interventions.

Keywords: DSM; addiction; gambling disorder; illegal acts; predictors.

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Online Gambling: A Systematic Review of Risk and Protective Factors in the Adult Population

• Review Paper
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• Published: 14 November 2023
• Volume 40 , pages 673–699, ( 2024 )

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• Michela Ghelfi   ORCID: orcid.org/0009-0006-3479-0946 1 ,
• Paola Scattola 2 ,
• Gilberto Giudici 2 &
• Veronica Velasco   ORCID: orcid.org/0000-0003-1890-5564 1  

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In recent decades, internet gambling has seen strong growth and diffusion due to intrinsic characteristics that make it particularly attractive to players (accessibility, anonymity, variety of games). This paper aims to present the current state of knowledge of the risk and protective factors of online gambling. A literature search conducted in the PubMed, PsychInfo, and Scopus databases found 42 articles, which were included in the review. Methodological aspects and risk and protective factors were analysed cross-sectionally. The results concerning risk and protective factors were distinguished by the level of analysis: individual, relational, and contextual. Two types of comparisons were considered: online vs. offline gamblers and online nonproblematic vs. problematic gamblers. The results of the two comparisons were juxtaposed to analyse their consistency and the different associations with factors. In general, the review showed that risk factors and variables at the individual level are investigated to a greater extent, while protective factors at the relational and contextual level need more in-depth study in future research. More specifically, this review found that even if online and offline gamblers shared most risk and protective factors, there are variables that they would not have in common. These factors could be important to consider in preventive interventions aimed at online gamblers and online problematic gamblers.

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Introduction

Gambling is defined as a form of entertainment centred on the wagering of any kind of valuable object or possession on a game or event, whose outcome is predominantly random (Boyd & Bolen, 1968 ). Since the beginning of time across all cultures and societies, gambling has been one of the most widespread leisure activities, and that has not changed. For many people, gambling is an enjoyable activity that has no repercussions on their lives; in contrast, for others, gambling may lead to addiction (Serpelloni, 2013 ). Previous studies have shown that the prevalence of adult problem gamblers is between 0.12 and 5.8% worldwide (Calado & Griffiths, 2016 ). Moreover, gambling has grown exponentially in recent decades, and accessibility, participation and expenditures are markedly increasing, as never before (Abbott, 2020 ). For these reasons, problem gambling is considered a socially relevant issue. It compromises public health by negatively impacting the wellbeing of individuals, their network of relationships and society as a whole. Interventions and policies, both from the point of view of care and treatment and by preventing its spread, are necessary.

Over the past 20 years, so-called internet or online gambling has grown exponentially mainly due to technological innovation (Gainsbury et al., 2012 ; Kim & King, 2020 ). Online gambling includes all forms of gambling conducted on the internet via different devices, such as laptops, mobile phones, tablets and digital TVs (Gainsbury et al., 2013 ). Online gambling represents an even more challenging phenomenon than offline gambling, as it is extremely widespread and characterized by more risk that make control, prevention and intervention complicated (Gainsbury, 2012 ). Moreover, online gambling has specific features that make it notably advantageous compared to land-based gambling: easier accessibility, convenience (less time and no travel are required), time flexibility (available 24 h a day), higher interactivity and continuity and ensured privacy (Gainsbury, 2012 ; Gainsbury et al., 2013 ). Additional reasons that make internet gambling more attractive to gamblers are the opportunity to create profiles that can hide one’s real identity and to play alone or interact with others through instant chats and forums (Hing et al., 2014 ).

This phenomenon has been impacted by COVID-19. Land-based gamblers have experienced massive changes during lockdowns due to the closure of gambling venues and the suspension of sports events. The pandemic has reduced overall gambling entries but has prompted land-based players to shift to internet gambling (Hodgins & Stevens, 2021 ). Meanwhile, the most recent literature regarding the effects of coronavirus on online gambling report no change in online gamblers’ play but no significant increase in this mode of gambling (Brodeur et al., 2021 ; Hodgins & Stevens, 2021 ). Nonetheless, higher levels of problem gambling are reported among those who have increased their gambling, and there is a strong association with mental health problems and substance use. Given these concerns, exacerbated by the COVID-19 pandemic, the diffusion of online gambling should be carefully monitored.

To design effective interventions and policies, it is essential to know the risk and protective factors associated with a phenomenon (Coie et al., 1993 ). However, the literature regarding the risk and protective factors of online problem gambling is not comprehensive. Most articles have focused on identifying risk and protective factors of problem gambling, especially among offline gamblers, or without even distinguishing them from online gamblers. Furthermore, most of the work concerning risk and protective factors has addressed the adolescent population (Dickson et al., 2008 ; Dowling et al., 2017 ), while little has targeted the adult population.

The most recent review regarding the risk and protective factors of internet gambling in the adult population was published by Gainsbury ( 2015 ), and it focuses on the association between online and problem gambling by comparing internet gambling with land-based gambling. However, this is not a systematic review, and no information is given about the methodology used. Given that most gamblers are not problematic, it is be important to better understand if there are differences between gamblers who choose to gamble online, without necessarily focusing on problematic gamblers. Moreover, considering the rapidly growing rate of this phenomenon, it seems necessary to update the knowledge about it to keep up with the changes.

This paper aims to review the knowledge and evidence about the factors that influence the likelihood of being an online gambler and developing a problematic mode of gambling among the adult population. To synthesize and systematize the results regarding risk and protective factors, two types of comparisons were made: comparison of factors that distinguish offline from online gamblers and comparison of online nonproblematic gamblers with online problematic gamblers. In addition, a further comparison was carried out to highlight whether similarities or differences emerged with respect to the factors studied between the first and second comparisons.

Search Strategy

To investigate knowledge about the risk and protective factors of online gambling, a systematic literature search was conducted in three different academic databases: PubMed, PsychInfo, and Scopus. Analogous syntaxes were launched limited to peer-reviewed articles only. The main keyword was “ gambling ” combined with “ online, internet, interactive ” and “ risk factors, protective factors, predictors, correlates ”. For clarification, the syntax entered in PsychInfo was (ab(online) OR ab(internet) OR ab(interactive)) AND ab(gambl*) AND (ab(risk factor*) OR ab(protect factor*) OR ab(promotive factor*) OR ab(predictor*) OR ab(correlate*)). Additional relevant publications were added based on the reference lists of selected papers and consultations with some experts in the gambling field. This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2015 Checklist (Moher et al., 2016 ).

Inclusion Criteria

The literature search was limited to peer-reviewed studies published in English between 2010 and 2020. The decision to investigate only this decade is to focus on the current state of knowledge of a phenomenon that, especially in recent years, is spreading significantly. The eligible articles met the following inclusion criteria in terms of Population Intervention Comparison Outcome (PICO): the reference population (P) was composed of adult online gamblers (age > 18), and risk factors and/or protective factors (I) at any level (individual and environmental) were investigated, excluding those related to biological determinants. Regarding the research outcome (O), outcomes related to all degrees of addiction, severity (nonproblematic, problematic, pathological), and risk of online gaming (low, medium, high) were included. Regarding the type of comparison (C) analysed, only articles comparing online gamblers with offline gamblers (C1) and/or online nonproblematic gamblers with online problematic gamblers (C2) were included.

Study Selection, Data Extraction and Analysis

Two independent evaluators screened the studies and extracted the data. The selection of articles was divided into two stages. First, studies were selected by reading the title and abstract, and those that were not relevant were excluded. Once the two researchers compared their choices, only those studies considered potentially eligible by both researchers were retained. The second phase consisted of full-text reading and application of the eligibility criteria. In cases of disagreement, the article was discussed, and a consensus was reached. After selecting the papers, the following data were extracted: aim of the study, method and type of article, sample characteristics (size, representativeness, response rate, recruitment method), tools and analysis used, control or comparison group, country, population and subpopulation, variables investigated, risk and protective factors. The extraction of population type and subpopulation concerned only sociodemographic characteristics. Data were extracted, and the narrative was synthetized by 2 authors and discussed and revised by another author. Once data extraction was performed, an initial stage of analysis was carried out. According to the main aim, each paper was categorized by the type of comparison (online vs. offline, nonproblem online versus problem online, both), the level of analysis studied (individual, relational, contextual), and the type of factors investigated (protective or risk factors). Papers were not categorized by the subpopulation of gamblers in terms of type of gambler (poker players, sport bettors, etc.) to investigate the differences between online and offline gamblers net of the influence each game type could exert. The group discussed the data, and the results were based on the consensus reached.

The review results are presented below. First, the search results and the screening process are shown. Second, there is a brief presentation of the characteristics of the included papers in terms of methodology. Third, the analysis of risk and protective factors reported in the included articles is presented. In this section, the factors associated with online gambling are analysed and subdivided according to the level of analysis (individual, relational and environmental). To synthesize and systematize the results regarding risk and protective factors, two types of comparisons were made: comparison of factors that distinguish offline gamblers from online gamblers (C1) and comparison of nonproblematic online gamblers from problematic online gamblers (C2). In addition, a further comparison was carried out to highlight whether similarities or differences emerged with respect to the factors studied between the first and second comparisons (C3). The results are systematized and presented in tables at the end of the paper, see Appendix A.

Search Results and Flowchart

Figure  1 represents the flowchart of the screening process. In total, 785 papers were retrieved through the database search, and deleting duplicates resulted in 420 unique citations. The first step, which consisted of screening studies by title and abstract, resulted in 52 eligible articles. Furthermore, 12 studies were retrieved from reference lists and gambling experts.

PRISMA Flow diagram

The second step involved selecting studies via full-text screening in relation to PICO criteria. Of the 64 eligible articles, 42 were included in the review. Appendix B includes a table with the title, the authors, and the year of publication of the articles included in the review in chronological order from the most recent.

Features of Selected Studies

Almost all of the selected studies were conducted in Western countries (Europe, the United Kingdom, Australia, Canada, United States of America), whereas only one was conducted in Asia (Macau) (Wu et al., 2015 ).

The young adult population (approximately 18–25 years) is the sample population in 8 papers) (1), most of which addressed the university population (Griffiths et al., 2010 ; Harris et al., 2013 ; Hopley & Nicki, 2010 ; MacKay & Hodgins, 2012 ; Mihaylova et al., 2013 ; Shead et al., 2012 ; Wu et al., 2015 ).

Quantitative methodology is used in all of the articles, mostly self-administered online questionnaires. Two studies used a mixed-method approach integrating quantitative data with semistructured interviews (Granero et al., 2020 ; Schiavella et al., 2018 ). Nearly all of the studies are cross- sectional. As these studies relate to a single measurement, the direction of the relationship between the variable and the outcome is not always clearly recognizable. Only 3 papers are longitudinal and consider different time periods: 30 days (Goldstein et al., 2016 ), and 2 years (Braverman & Shaffer, 2012 ; Dufour et al., 2020 ).

Some articles use random and representative samples of the population. These studies are usually part of a wider national survey. However, the sample is self-selected in most of the papers.

In most of the articles, recruitment took place on the internet, given the characteristics of the sample. The participants were recruited mostly through online advertisements on specialized websites and forums and on social networks. Another remote method commonly used concerned online wagering operators, who sent an email invitation to a randomly selected user sample. In many studies, participants were recruited using both online and offline methods, the latter including advertising in newspapers, on television, on the radio or by telephone or posters at gambling venues.

Different types of analysis were carried out for different purposes. Among the main ones are the identification of gamblers’ groups through cluster analysis (Braverman & Shaffer, 2012 ; Dufour et al., 2013 , 2020 ; Granero et al., 2020 ; Khazaal et al., 2017 ; Lloyd et al., 2010a ; Perrot et al., 2018 ) comparison between groups using bivariate or multivariate analysis, and the exploration of population characteristics through descriptive analysis.

Risk and Protective Individual Factors

Sociodemographic information.

When compared to offline gamblers, online gamblers were more likely to be male male (Dowling et al., 2015 ; Edgren et al., 2017 ; Gainsbury et al., 2012 ; Goldstein et al., 2016 ; Griffiths et al., 2011 ; Harris et al., 2013 ; Kairouz et al., 2012 ; Lelonek-Kuleta et al., 2020 ; MacKay & Hodgins, 2012 ; Mihaylova et al., 2013 ; Redondo, 2015 ; Shead et al., 2012 ; Wood & Williams, 2011 ; Wu et al., 2015 ), younger (Dowling et al., 2015 ; Edgren et al., 2017 ; Gainsbury et al., 2013 ; Griffiths et al., 2011 ; Hubert & Griffiths, 2018 ; Kairouz et al., 2012 ; Lelonek-Kuleta et al., 2020 ; Redondo, 2015 ; Wardle et al., 2011 ; Wood & Williams, 2011 ; Wu et al., 2015 ), with a higher level of education (Dowling et al., 2015 ; Gainsbury et al., 2015b ; Griffiths et al., 2011 ; Redondo, 2015 ; Wardle et al., 2011 ; Wu et al., 2015 ) and a higher income (Dowling et al., 2015 ; Edgren et al., 2017 ; Gainsbury et al., 2012 ; Wardle et al., 2011 ; Wood & Williams, 2011 ; Wu et al., 2015 ). Among the articles included in the review, there is a strong homogeneity of results for these four factors. The only exception is in the article by Lelonek-Kuleta et al. ( 2020 ), in which a lower income was most likely associated with internet gamblers.

In addition to these widely studied factors, further sociodemographic factors are investigated to a lesser extent. For example, regarding gamblers’ occupation, it is shown that having paid employment (Dowling et al., 2015 ; Wardle et al., 2011 ) and a full-time job (Edgren et al., 2017 ; Gainsbury et al., 2012 , 2013 ; Wood & Williams, 2011 ) is more likely reported by internet gamblers than land-based gamblers, although Hubert and Griffiths ( 2018 ) (26) report contradictory results. Contrasting results are also reported regarding gamblers’ marital status or relationships. According to 3 articles, online gamblers are more likely to live with a stable partner (Dowling et al., 2015 ) or to be married (Hubert & Griffiths, 2018 ; Wood & Williams, 2011 ), whereas other studies report that they are less likely to be married Hubert & Griffiths, 2018 ; Wood & Williams, 2011 ) and more likely to be single (Griffiths et al., 2011 ; Kairouz et al., 2012 ) or cohabiting (Kairouz et al., 2012 ). The place of residence was investigated by two different authors, who came to opposite conclusions. According to Lelonek-Kuleta et al. ( 2020 ), living in rural areas (rather than a city or town) increases the likelihood of being an online gambler. In contrast, according to Gainsbury et al., ( 2015a , 2015c ), internet gamblers are more likely to live in a metropolis. Another variables have been investigated: having dependent children, which is associated with both online and offline gambling (Dowling et al., 2015 ; Hubert & Griffiths, 2018 ).

The comparison between online problematic and nonproblematic gamblers shows partially different results. It was found that online problem gamblers are more likely to be male (Gainsbury et al., 2014b ; Hing et al., 2017 ; McCormack et al., 2013b ; Wu et al., 2015 ), younger (Gainsbury et al., 2013 , 2014c , 2015c ; Granero et al., 2020 ; Hing et al., 2017 ), less educated educated (Gainsbury et al., 2015c ; Schiavella et al., 2018 ), have a lower income (Granero et al., 2020 ; Hing et al., 2017 ), be unemployed or rarely professionally active (Barrault et al., 2017 ; Gainsbury et al., 2014c , 2015c ; Granero et al., 2020 ), unmarried (Gainsbury et al., 2015c ; Granero et al., 2020 ; Khazaal et al., 2017 ) and have dependent children (Lelonek-Kuleta et al., 2020 ), than online nonproblematic gamblers.

A few articles have reported opposite results. Regarding gambler’s sex, Gainsbury et al. ( 2014c ) reported that chasing losses, a behaviour associated with pathological gambling, is more frequent among women than men. An interesting result emerges from the comparative study by Edgren et al. ( 2017 ) in which female online gamblers were found to be at higher risk than men, both of higher expenditures on gambling and of being more problematic gamblers. Furthermore, in Khazaal et al. ( 2017 ), the higher percentage of women was within the most problematic cluster. The latter study is also in contrast with the majority of articles about the age variable, reporting that the most problematic cluster is characterized by a higher age average compared to the less problematic clusters.

Gambling Patterns and Behaviours

Regarding gambling behaviour, differences were found between online and offline gamblers, and two variables were particularly salient: intensity and variability of gambling. Compared to land-based gamblers, internet gamblers were more likely to gamble more frequently (high intensity) (Barrault & Varescon, 2016 ; Dowling et al., 2015 ; Dufour et al., 2013 ; Gainsbury et al., 2012 , 2013 ; Hubert & Griffiths, 2018 ; Kairouz et al., 2012 ; MacKay & Hodgins, 2012 ; Mihaylova et al., 2013 ; Shead et al., 2012 ). Consistent results from different studies state that high variability in gambling activities is associated more with online gambling than offline gambling (Dowling et al., 2015 ; Edgren et al., 2017 ; Gainsbury et al., 2012 , 2013 ; Kairouz et al., 2012 ; MacKay & Hodgins, 2012 ; Mihaylova et al., 2013 ; Shead et al., 2012 ; Wardle et al., 2011 ; Wood & Williams, 2011 ). Furthermore, online gamblers are more likely to gamble for longer periods of time and to report higher expenditures than offline gamblers (Dowling et al., 2015 ; Dufour et al., 2013 ; Goldstein et al., 2016 ; Kairouz et al., 2012 ; Wood & Williams, 2011 ), as well as higher indebtedness (Mihaylova et al., 2013 ; Wood & Williams, 2011 ). In contrast with these results, Barrault and Varescon ( 2016 ) state that longer sessions, higher bets and winnings are more likely reported by offline gamblers than online gamblers.

In addition to higher intensity, variability, and expenditures, online gamblers are more likely to be at risk of problem gambling gambling (Dufour et al., 2013 , 2020 ; Goldstein et al., 2016 ; Griffiths et al., 2011 ; Harris et al., 2013 ; MacKay & Hodgins, 2012 ; Wardle et al., 2011 ; Wood & Williams, 2011 ; Wu et al., 2015 ). In fact, internet gamblers have higher levels on the Problem Gambling Severity Index (PGSI) than land-based gamblers (Gainsbury et al., 2014b ; Kairouz et al., 2012 ). In the study by Wu et al. ( 2015 ) conducted in Macao, more symptoms of pathological gambling were reported by online gamblers in both selected samples: one representative of the adult population and the other representative of university students. Furthermore, two articles show that the first gambling experience for online players was at a younger age than for land-based players (Wu et al., 2015 ): approximately 19 years for online players and 24 years for offline players (Dowling et al., 2015 ), highlighting that an earlier onset of gambling behaviour is more likely to be associated with the online mode (Granero et al., 2020 ).

Most of the variables reported above are in common with the risk factors for online problem gambling. In fact, problematic gamblers’ behaviour is more likely characterized by greater involvement: high frequency (intensity) (Barrault & Varescon, 2016 ; Braverman & Shaffer, 2012 ; Dufour et al., 2013 ; Gainsbury et al., 2014c ; Griffiths et al., 2010 ; Hing et al., 2017 ; Hopley & Nicki, 2010 ; LaPlante et al., 2014 ; MacKay & Hodgins, 2012 ; McCormack et al., 2013a ; McCormack et al., 2013b ), participation in several different gambling forms (high variability) (Braverman & Shaffer, 2012 ; Gainsbury et al., 2014b , 2015a , 2015c ; Hing et al., 2017 ; LaPlante et al., 2014 ; Lloyd et al., 2010a , 2010b ; McCormack et al., 2013b ; Perrot et al., 2018 ), high expenditure (Barrault & Varescon, 2013b ; Barrault & Varescon, 2016 ; Dufour et al., 2013 ; Gainsbury et al., 2014b , 2014c , 2015c ; Griffiths et al., 2010 ) and indebtedness (Gainsbury et al., 2012 , 2016 ). In terms of the effects on expenditures, as was assumed in Gainsbury et al. ( 2015c ), compared to nonproblematic or at-risk gamblers, problem gamblers reported a greater amount of money lost through gambling and a greater amount of household debt. An additional gambling behaviour more likely associated with online at-risk gamblers is the longer session duration duration (Barrault & Varescon, 2013a , 2013b , 2016 ; Griffiths et al., 2010 ; McCormack et al., 2013b ). Although investigated by a few articles, problem gambling risk factors also include early onset of gambling (Granero et al., 2020 ; Wu et al., 2015 ), the use of mobile devices compared to computers (Gainsbury et al., 2016 ), gambling for more than 9 years, not entertaining virtual interactions (Khazaal et al., 2017 ) and gambling in solitude (McCormack et al., 2013b ). Finally, four studies identify being a “mixed-mode” gambler who gambles both online and offline as a risk factor for problem gambling gambling (Dufour et al., 2013 ; Gainsbury et al., 2015b ; MacKay & Hodgins, 2012 ; Wardle et al., 2011 ). Mixed-mode gamblers had more symptoms and higher levels of severity than internet-only gamblers. However, this evidence needs further investigation and discussion, since only a minor number of the studies uses the mixed mode method in addition to the dichotomy of online versus offline.

Risky Behaviours

As reported in previous paragraphs, gambling is often associated with other types of risk behaviour, such as substance misuse. Even though this correlation is valid for all kinds of gamblers, what emerges from review studies is that online gamblers are more likely to use or misuse substances than offline gamblers both while gambling and at other times (Dowling et al., 2015 ; Gainsbury et al., 2014b ; Griffiths et al., 2011 ; Harris et al., 2013 ; Kairouz et al., 2012 ; Mihaylova et al., 2013 ; Shead et al., 2012 ; Wood & Williams, 2011 ). According to Gainsbury et al. ( 2014b ), a significantly higher proportion of internet gamblers report drinking and smoking while engaging in land-based gambling compared to offline gamblers. In contrast, in Goldstein et al. ( 2016 ), consuming more substances while gambling was associated with being less likely to be online gamblers. In the internet gamblers group, more people reported hazardous drinking (Dowling et al., 2015 ; Griffiths et al., 2011 ), alcohol consumption and addiction (Kairouz et al., 2012 ; Mihaylova et al., 2013 ). In relation to the use of other substances, online gamblers are more likely to consume and misuse regular drugs (Dowling et al., 2015 ), illicit drugs (Mihaylova et al., 2013 ) and cannabinoids (Dowling et al., 2015 ; Kairouz et al., 2012 ). According to Gainsbury et al. ( 2014b ), offline gamblers are more likely to be nonsmokers than online gamblers; concordantly, a significantly higher proportion of online gamblers smoked daily than land-based gamblers.

The relevant use of alcohol, tobacco and drugs represents a risk factor for the development of a problematic online gambling patterns (Gainsbury et al., 2014b ; Granero et al., 2020 ; Lloyd et al., 2010a ), even if in some studies, only the number of cigarettes smoked is higher in the riskiest gamblers (Harris et al., 2013 ; McCormack et al., 2013b ). As reported above, it seems that consumption of alcohol or other substances during gambling is more likely associated with online problem gamblers than nonproblem gamblers (Gainsbury et al., 2015c ; Harris et al., 2013 ; Hing et al., 2017 ; McCormack et al., 2013b ).

Risky behaviours related to gambling do not end with excessive substance use; there are other behaviours associated with online and problem gambling, for example, the excessive use of the media. Among the factors that are more likely associated with online gambling are the early use of computers (Hubert & Griffiths, 2018 ) and being experienced in computer gaming (Edgren et al., 2017 ). Concordantly, Lelonek-Kuleta et al. ( 2020 ) found that people with lower daily internet use are less involved in online gambling. The relevant involvement in gaming was also found to be a risk factor for the development of a problematic gambling pattern (Khazaal et al., 2017 ).

Deliberate self-harm is another risky behaviour that, according to Lloyd et al. ( 2010a ), is more prevalent among the most problematic cluster of online gamblers (multiactivity players) compared to others.

Health and Wellbeing

Physical health.

Health and well-being are scarcely investigated in the papers included in this review, and their results are almost contradictory. For example, in Wardle et al. ( 2011 ), online gamblers were more likely to report that their general health was better than that of land-based gamblers. Regarding physical wellbeing, Shead and colleagues ( 2012 ) showed that land-based university student gamblers were more likely to be normal weight, while internet gamblers were more likely to be underweight, overweight, or obese. Furthermore, a physical disability or a significant mental health problem is a predictor of internet gamblers more than offline gamblers (Wood & Williams, 2011 ). According to Redondo ( 2015 ), online gamblers are less interested in their future personal health; in fact, they are more likely to engage in unhealthy activities.

In line with the above, the only risk factor for the development of a pathological mode of gambling emerged from the study by McCormack et al. ( 2013b ) with a sample of online gamblers. Problem gamblers were found to be more likely to report a disability than nonproblem gamblers.

Psychological distress and emotions

Regarding psychological well-being, only a few studies have reported significant differences between online and offline gamblers. Gainsbury et al.’s ( 2014b ) paper contends that online gamblers are more likely to experience psychological distress than land-based gamblers. A further relevant result was shown by Goldstein et al. ( 2016 ), who monitored the mood of a sample of young adults for 30 consecutive days. The data collected show that those who used the internet to gamble experienced greater negative affect, with higher frequency and intensity, during the observation compared to nononline gamblers.

The high occurrence of cross-sectional studies does not allow us to clearly define the relationship’s direction between psychological distress and problem gambling. It is difficult to establish whether the former is a risk factor or an outcome of the latter. For example, it is unclear whether a high level of psychological distress is a consequence of frequent gambling or conversely whether people with psychological distress are particularly attracted to gambling. Predictably, a higher level of psychological distress was found in online gamblers more at risk of problem gambling than in low-risk gamblers (Gainsbury et al., 2014b ; Granero et al., 2020 ; Hing et al., 2017 ; Hopley & Nicki, 2010 ). Anxiety and depression were the main experiences studied and reported by pathological gamblers at higher rates (Barrault & Varescon, 2013a ; Barrault et al., 2017 ; Hopley & Nicki, 2010 ; Khazaal et al., 2017 ). In addition, mood disturbances such as hypomanic experiences and mood elevation are reported to a greater extent in the most problematic cluster (Lloyd et al., 2010a ). Additional emotional states that are more likely associated with a riskier mode of gambling are dissatisfaction with life (Wu et al., 2015 ) and feelings of loneliness (Khazaal et al., 2017 ). Furthermore, problem gamblers and at-risk gamblers were significantly more likely to feel euphoria, excitement, anger, and happiness while gambling (McCormack et al., 2013b ). According to the authors, problem gamblers are more likely to experience extreme emotional highs and lows than nonproblem gamblers. In addition, having good emotional intelligence serves as a protective factor against the development of a problematic mode of gambling (Schiavella et al., 2018 ). A high level of emotional awareness, assertiveness, self-care (understanding and acceptance of self), independence (no emotional dependency), and self-actualization are all aspects that decrease the risk of experiencing a gambling disorder.

Personality Characteristics and Cognitive Components

Personality characteristics.

Regarding personality characteristics, little has been reported for online vs. offline gamblers. According to Redondo ( 2015 ), online gamblers are more likely to be characterized by a low degree of sociability and a higher level of frugality.

Variables associated with personality were more relevant in the comparison between those who were at risk of developing problematic gambling. Impulsivity, or the tendency to implement behaviours without considering the possible consequences (Zuckerman & Kuhlman, 2000 ), is the most widely investigated personality trait and appears to be particularly associated with pathological gambling patterns (Barrault & Varescon, 2013b , 2016 ; Hopley & Nicki, 2010 ; Khazaal et al., 2017 ; Moreau et al., 2020 ). Other personality traits that increase the likelihood of incurring a problematic mode of gambling are the predisposition to boredom (Hopley & Nicki, 2010 ) and the lack of premeditation (Khazaal et al., 2017 ).

In Granero et al. ( 2020 ), it was generally found that those who have a dysfunctional personality profile (characterized, for example, by high scores in the novelty-seeking dimension) have a higher likelihood that their gambling will result in a disorder. Conversely, people with functional personality characteristics have a lower likelihood of experiencing problematic gambling. In addition, high scores on the trait of self-direction, which is the ability to adjust behaviour to the demands of the situation to achieve their goals, and in the trait of cooperativeness are considered protective factors associated with adaptive emotional and cognitive responses (Granero et al., 2020 ).

Cognitive Components

Some dysfunctional thinking mechanisms are found to have an influence on the likelihood of being an online gambler. Compared to offline gamblers, internet gamblers are more likely to have cognitive distortions of two main types: the illusion of control and perseverance (Dufour et al., 2020 ; MacKay & Hodgins, 2012 ). In Wood and Williams ( 2011 ), the illusion of being able to manipulate the outcome of the game has been identified as a risk factor.

The presence of cognitive distortions about gambling increases the likelihood of developing problematic gambling (Barrault & Varescon, 2013a ; Gainsbury et al., 2014c , 2015c ; MacKay & Hodgins, 2012 ; Moreau et al., 2020 ; Schiavella et al., 2018 ). Comparing low-risk gamblers and those who are pathological, the latter report significantly greater levels in all five types of cognitions analysed in the Gambling Related Cognition Scale (GRCS): gambling-related expectancies, the illusion of control, predictive control, the perceived inability to stop gambling, and interpretative bias. Additional risk factors found in the analysis of poker players and associated with problem gambling include episodes of dissociation while playing (Hopley & Nicki, 2010 ) and frequent tilt episodes (Moreau et al., 2020 ).

Representations, Attitudes and Motivation to Gamble

Representations and attitudes.

A gambler's attitude towards gambling has been found to be relevant in influencing the choice of gambling mode. Articles suggest that having a positive attitude towards online gambling increases the likelihood of gambling on the internet (Gainsbury et al., 2012 ; Harris et al., 2013 ; Wood & Williams, 2011 ; Wu et al., 2015 ). In Gainsbury et al. ( 2012 ), internet gamblers experienced higher scores in items investigating the morality, legality, and cost‒benefit of online gambling. In addition to attitudes, a higher level of trust in the internet was more likely to be associated with online gamblers than with offline gamblers (Redondo, 2015 ). In the article by Harris et al. ( 2013 ), a significant difference emerged between the group of online gamblers and the group of land-based gamblers: internet gamblers reported higher scores for the items related to confidence in the security of both online payments and websites than land-based gamblers. Moreover, Redondo ( 2015 ) showed that online gamblers have a lower religious orientation than offline gamblers and are less interested in the future of the environment, so they participate less in environmentally responsible activities.

Attitudes towards gambling also appear to influence the likelihood of developing problematic gambling. High-risk internet gamblers are found to have a more negative attitude towards gambling (Harris et al., 2013 ; Hing et al., 2017 ). In the article by Gainsbury et al. ( 2015c ), problem gamblers were more likely to believe that the harm of gambling outweighed the benefits, that it was an immoral activity and that all forms of gambling should be illegal. The same result was presented by Hing et al. ( 2017 ), who found that problem gamblers reported negative attitudes. This result seems to contrast with findings regarding internet trust, which is associated with a higher likelihood of being problem gamblers (Harris et al., 2013 ).

Motivations to Gamble

Among the motivations that drive a person to gamble, four main reasons are investigated: enhancement, coping, social, and financial. Motivations of enhancement include reasons related to the positive feelings and excitement aroused by gambling; social motivations refer to the willingness to gamble to socialize, spending time with friends or celebrating; coping motivations relate to gambling to relax, to forget problems or because it helps one feel better; and financial motivations refer to the need to get some money, the possibility of winning large sums of money, or wanting to earn money (Lloyd et al., 2010b ; Stewart & Zack, 2008 ). Compared to land-based gamblers, the gambling motivations reported most often by online gamblers are coping reasons (regulating internal state) (Dowling et al., 2015 ; Goldstein et al., 2016 ), financial reasons (Barrault & Varescon, 2016 ) and to satisfy a need for a challenge or to show skills (Dowling et al., 2015 ; Goldstein et al., 2016 ). Those who gamble for social reasons (Barrault & Varescon, 2016 ), because of the positive feelings it elicits (Dowling et al., 2015 ), or because they believe this activity provides enjoyable social encounters (Goldstein et al., 2016 ) are more likely to belong to the group of land-based gamblers. Goldstein et al. ( 2016 ) analysed the specific motivations for which online gambling is initiated compared to offline gambling. Gamblers were more likely to initiate online activities to win money, to be entertained, or to demonstrate their ability and to discontinue online activities due to feeling bored, tired and distressed. Online gambling activities were less likely to be initiated for social reasons, or because they felt lucky (Goldstein et al., 2016 ). According to Hubert and Griffiths ( 2018 ), comparing online to offline problematic gamblers, the results show that the former are more likely to gamble for fun and leisure.

The same main motivations emerged when investigating online gamblers and comparing them across degrees of severity. Problem gamblers are more likely to report reasons related to the feelings that gambling causes, such as excitement (Gainsbury et al., 2014c ), financial aspects (Gainsbury et al., 2014c ; Khazaal et al., 2017 ) or occupational aspects, such as the desire to make money from gambling (Barrault et al., 2017 ), and coping, as the aim to relax (Khazaal et al., 2017 ). In contrast to what was previously stated regarding the possibility that coping motivations act as a risk factor, in the article by Gainsbury et al. ( 2014c ), it appears that gambling to relax is reported more by nonproblem gamblers. In addition, nonproblem gambling appears to be associated most often with leisure and coping purposes, such as for pleasure, experiencing positive emotions, a distraction from everyday life and thus relaxation (Barrault & Varescon, 2013b , 2016 ) and as an occasion of social gathering (Khazaal et al., 2017 ).

The aspects associated with the intrinsic characteristics of online gambling, which were discussed in depth in the introduction, are investigated to a lesser extent in this review’s papers. Compared to offline gamblers, online gamblers report greater motivation due to accessibility, availability, variability in sites and activities, anonymity, and prevention/protection (Hubert & Griffiths, 2018 ). In addition, greater accessibility and anonymity are two of the reasons more likely to be reported by problem gamblers than by nonproblem gamblers (McCormack et al., 2013b ).

Risk and Protective Relational and Contextual Factors

Relational factors.

The choice of gambling modality appears to also be influenced by aspects related to the network of the gambler’s relationships, even if they are poorly investigated in comparison to individual factors. Studies show that low quantity and quality of the relationships of those who gamble play a role in increasing the likelihood of being internet gamblers. An additional factor associated with the online mode is reporting the subjective presence of issues within the household due to gambling (Mihaylova et al., 2013 ). At the relational level, a single factor has been identified that increases the likelihood of developing problematic gambling: the presence of gamblers and problem gamblers among family members. This result has been reported by two different authors who considered the general adult population (Lloyd et al., 2010a ) and university students (Harris et al., 2013 ).

Contextual Factors

The surroundings and life contexts to which a person belongs play an important role in influencing gambling, as do individual and relational factors. Within the selected articles, variables acting at the contextual level were scarcely investigated. The university context, among all, is the only setting that has been investigated and for which there is evidence of a risk factor. The presence of academic issues in the population of university students appears to increase the likelihood that not only they will use the internet to gamble (Mihaylova et al., 2013 ), but also they will become problem gamblers (Harris et al., 2013 ).

This paper provides a synthesis of knowledge regarding the risk and protective factors of online gambling in the adult population. From the analysis carried out, several critical elements emerge, which may offer indications for future studies. Regarding the methodology used in the studies, two critical issues emerge concerning the population and the method. Most of the papers use nonrepresentative samples. For future research, it would be desirable to use representative samples of the population. In addition, most of the papers are cross-sectional studies, whereas it would be desirable to conduct longitudinal studies to achieve a greater understanding of the relationship between variables. It is necessary to highlight that in most papers, the sample was mainly composed of men, no women. Studies that included women reported that these gamblers were at greater risk of developing problematic gambling and were more attracted to internet gambling. This topic was explored in a qualitative study by Corney and David ( 2010 ) that focuses on the motivations of female online gamblers. This article suggests that aspects related to ease of access and anonymity of gambling are particularly relevant for women. In fact, the possibility of gambling from home and remaining anonymous make online gambling more attractive to women, as they perceive it to be safer and less intimidating. For these reasons, it would be relevant in future research to use a representative sample.

Several factors were identified in the review. Socioanagraphic variables are among the most studied in both comparisons. Gender, age, level of education, occupation, income and marital status are largely investigated. Being male and younger seem to be associated more with online gamblers than offline gamblers and with problematic online gamblers than nonproblematic gamblers. Moreover, a high level of education, income, and job status are more likely associated with online gamblers than offline gamblers. At the same time, looking at online gamblers, it seems that these factors are more related to less problematic gamblers than problematic gamblers. Other contradictory results regard marital status or the sentimental relationship. It seems that having a stable partner is more likely associated with online gambling than offline gambling, even though it is more associated with nonproblem gamblers than problem gamblers. Having dependent children is more likely associated with online and problematic gamblers, but it is studied by only a few papers.

Gambling patterns and behaviors is the second most studied factors category. A relevant number of papers show that high intensity, high variability, and high expenditures in gambling are more likely associated with online gamblers and represent risk factors for problematic gambling. The same association is reported concerning long session duration and having an early onset of gambling behaviour. Some factors are studied only for the second comparison. Among these, solitary gambling (not using virtual chats or forums), being a mixed-mode and long-time gambler, using mobile devices to gamble, and having tilt episodes represent risk factors for problem online gamblers, even though only a few studies show these results.

Risky behaviours, such as the consumption of alcohol, drugs, and tobacco, are studied in both comparisons. The misuse of substances is more likely associated with online gamblers than offline gamblers and with online problematic gamblers than less problematic gamblers. Moreover, the same association is reported for high use of media, while deliberate self-harm is more likely to be found among problem online gamblers.

Factors related to physical well-being are poorly investigated, and mainly concern the comparison between online and offline gamblers. It seems that offline gamblers are more interested in engaging in healthy activities, are fitter and generally feel healthier than online gamblers.

Psychological dimensions are slightly investigated, and most of these papers study only the second comparison. Online problem gamblers are more likely to report psychological distress and anxious or depressive states than nonproblem gamblers. A smaller number of studies reported that negative moods, extreme emotions while gambling, and mood disturbance are more likely associated with online problem gamblers. However, one paper shows how high emotional intelligence (emotional awareness, assertiveness, self-care, independence, self-actualization) could act as a protective factor, but further investigation is needed.

Personality traits have not been extensively explored. High impulsivity is the most often studied factor, and it is associated most often with online problematic gamblers as much as having a dysfunctional personality. In contrast, online gamblers compared to offline gamblers seem to have a minor degree of sociability and a higher level of frugality, but it is only stated by a single paper. Concerning the cognitive components, the abundant presence of cognitive distortion in gambling (as the illusion of control) is more likely associated with online and online problematic gamblers than with offline and nonproblematic gamblers.

Attitude towards gambling has been found to be relevant in influencing the choice of gambling mode. Articles suggest that having a positive attitude towards online gambling is more likely associated with internet gambling, while a negative attitude is related more often with problem gambling. This result should be further investigated.

Among the different reasons to gamble, social motivations are more often related to offline and nonproblem gamblers, while financial reasons are more often associated with online and problem gambling. Contradictory results emerged regarding coping and pleasure reasons, and it is not clear how these motivations influence gambling behaviour, so further studies will be needed.

Scarce attention is given to relational factors and contextual factors. A few papers suggest that having rare and negative relationships is more likely associated with online gambling. Moreover, having family members who gamble could influence the likelihood of being a problem gambler. In addition, having problems in life contexts such as academia is reported mainly by people who gamble online and are problematic gamblers.

The results of the review regarding risk and protective factors show that risk factors are investigated to a greater extent than protective factors. This criticality highlights the need to strengthen research from a well-being-promotion approach to identify and then intervene on variables related to positive outcomes. In addition, among the levels of analysis studied in the literature, the most in-depth level concerns individual aspects, while both the relational and contextual levels are poorly investigated. Future research would need to embrace a psychosocial perspective that considers, at least equally, all types of levels, valuing the influence that the environment has on the individual. Moreover, some of the factors’ categories are scarcely investigated in the literature; for this reason, they need to be explored in greater depth. Examples include variables associated with physical well-being, emotional and social functioning, and interpersonal skills. One of the recurring themes among the categories concerns bonding with other people. In general, it appears that the presence of other people in different contexts of life acts as a protective factor for problematic gambling, while the absence of these represents a risk factor. Although the relational level is poorly investigated within the review, the positive influence of relationships is studied at the individual level. For example, being married or being in a relationship with a stable partner, and among the factors associated with gambling patterns, playing while in the company of others represents a protective factor. Similarly, sociality is also present in motivational aspects, and those who gamble to meet other people, celebrate, and be with friends are less likely to be problem gamblers. These results refer to the importance that the social sphere has on the individual, which is essential. This theme needs to be studied to a greater extent and to be taken into consideration from the point of view of intervention and prevention.

Most of the factor results are in line with what emerged from Gainsbury’s review ( 2015 ) and previous literature about risk factors for problem gambling. For example, several risk factors for problem gambling were confirmed: being male, being a young adult, having gambling behaviours characterized by high intensity, variability and high expenditures, gambling for long periods of time, having an early onset of gambling behaviour, misusing substances, and reporting psychological distress, impulsivity, and cognitive distortions related to gambling. Moreover, having academic and familiar issues or familiarity with gambling are risk factors for problem gambling. However, many other protective and risk factors emerged from this review, such as social support, healthy lifestyle, emotions, motivations and technology use and interactions with others. This review differs from Gainsbury's in that it attempts to use an additional and more systematized classification to the reading of risk and protective factors of gambling. Specifically, the papers included in the review are classified depending on two different comparisons: according to the degree of severity of online gambling and the differences between online and offline gambling. Including these two comparisons is crucial to account for the complexity of online gambling and the different targets involved. Analogies and differences emerged from these two comparisons, and specific needs of further investigations have been identified. For example, contradictory results emerged about gender differences, level of education influence, emotional skills, attitudes and motivational issues.

In conclusion, aiming to fill the literature gap on preventive factors for online gambling, the results of this literature review can provide the basis for developing efficient preventive strategies that go beyond responsible gambling options offered by gambling platforms (Gainsbury et al.,  2014a ; Velasco et al., 2021 ). These findings contribute to identifying the groups most attracted to online gambling and most vulnerable to the development of problem gambling. These people should be the focus of future research and targeted individualized interventions. From a more general prevention perspective, more coordination between research evidence, agencies, and institutions is needed to support policies and a social culture unfavourable to gambling to protect the health of online gamblers. Specifically, given the commonalities between risk and protective factors for online and offline gambling, it does not seem necessary to create new prevention interventions dedicated directly to online gambling. On the one hand, given the presence of aspects related only to online gamblers and given the differences in terms of socioanagraphic variables, it would seem to make sense to reevaluate some of the interventions to adapt them to these specificities. For example, given that even gamblers from populations considered less at risk (highly educated and employed) seem to be highly attracted to gambling, it would be important to target them with specific interventions or include them in a universal intervention. On the other hand, it appears that gamblers with fewer resources are more likely to become problematic gamblers and thus would need to be involved in indicated interventions to promote or enhance protective factors. More attention should be given to acknowledging and dealing with the taboo of female gamblers; despite being an extremely valuable topic, it was not covered much by the articles included in the review. Finally, the relevance of social relationships and sociality during gambling should be considered when designing online gambling preventive interventions. Online access to gambling facilitates solitary play and isolating habits, and social protective factors could be reduced.

Limitations of the Review

This review presents some limitations. No statistical processing typical of meta-analyses to assess the results has been included. However, the ability of this review to synthesize the evidence across a large body of literature offers a valid overview and some recommendations. Regarding the included studies, not all papers displayed the same level of methodological quality, and the criteria used for the studied population were quite different. Moreover, the literature lacks a clear and determined definition to distinguish online and offline gamblers. In fact, some authors consider that only those who exclusively use this mode are online gamblers, while others define them as such even if they mainly use the online mode but also gamble offline. Given the heterogeneity of the literature and the need to synthesize and systematize the results, the information regarding “exclusively internet gamblers” or “mixed mode gamblers” was included in the same “online gamblers” category regardless of the definition used by the authors. The reason behind this choice is that this distinction of exclusivity was made explicit only in a few papers, so we considered online gamblers who play at least partially online. Moreover, because there is no univocal and agreed definition to classify online gamblers depending on the intensity of gambling, we considered the category online gamblers without distinguishing the different definitions of the authors. For example, some authors consider online gamblers to be those who gamble at least once a year, others if the frequency is once a month, they were both just addressed as “online gamblers”. Furthermore, in the literature, there is no clear and shared definition and categorization of gamblers depending on the degree of severity of problem gambling. Some authors distinguish between low-, medium-, and high-risk gamblers, while others consider only nonproblem or problem gamblers. To synthesize, the results of the papers are read without valuing the intermediate degrees of risk, distinguishing only between problematic or nonproblematic gambling. Finally, given that only some papers considered only specific subpopulations of gamblers (e.g., poker players, sports bettors), the results of the papers were considered net of gambling types.

The aim of this paper was to review the knowledge and evidence about the factors that influence the likelihood of being an online gambler and developing a problematic mode of gambling in the adult population. The review synthesized and systematized the risk and protective factors associated with online gambling. Specifically, to do so, two types of comparisons were made: comparison of factors that distinguish offline from online gamblers and comparison of online nonproblematic gamblers from online problematic gamblers. In addition, a further comparison was carried out to highlight whether similarities or differences emerged in the results with respect to the factors studied between the first and second comparisons. The results of this work could be useful in suggesting directions for the development of prevention programs targeted at offline and online gamblers, which could be aimed at strengthening or increasing protective factors and limiting and reducing risk factors. Moreover, this review provides some suggestions for distinguishing characteristics more associated with online problem gambling and non-problem ones. Finally, this review found that even if most risk and protective factors are in common between online and offline gamblers, there are some variables that are not. These factors could be important to consider in project prevention interventions aimed at targeted online gamblers and online problematic gamblers.

Data availability

Data sharing is not applicable to this article as no new data were created or analysed in this study.

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Ghelfi, M., Scattola, P., Giudici, G. et al. Online Gambling: A Systematic Review of Risk and Protective Factors in the Adult Population. J Gambl Stud 40 , 673–699 (2024). https://doi.org/10.1007/s10899-023-10258-3

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Social influences normalize gambling-related harm among higher risk gamblers

Alex m. t. russell.

1 Experimental Gambling Research Laboratory, School of Health, Medical and Applied Sciences, CQUniversity, Sydney, NSW, Australia

Erika Langham

2 Centre for Indigenous Health Equity Research, School of Health, Medical and Applied Sciences, CQUniversity, Cairns, QLD, Australia

Nerilee Hing

3 Experimental Gambling Research Laboratory, School of Health, Medical and Applied Sciences, CQUniversity, Bundaberg, QLD, Australia

Background and aims

Social influences are key drivers of gambling, and can begin in youth through parental modeling and facilitation. Over time, social influence from friends and colleagues also becomes important. Social network analysis provides a method to measure the combined nature of these social influences. This study aimed to compare social influences across gambling risk groups, by examining key characteristics of the social networks, among Australian adults.

A total of 784 respondents (egos) reported their demographics, gambling behavior and gambling risk, as well as those of the 20 most influential people in their lives (alters). Egos also reported the strength of the connection between themselves and each of their alters, and between each pair of alters. Data were analyzed using egocentric social network analysis approaches.

Egos in higher risk groups reported more alters who gamble, including a higher proportion experiencing gambling-related harm. Relationship strength indicated that egos in higher risk groups tended to feel closer to their alters, regardless of whether the alter gambles or not. Network density (interconnectedness between alters) was greater for egos in higher risk groups.

Discussion and conclusions

The findings indicate that both gambling behavior and gambling-related harm are normalized through social connections. Greater interconnectedness in the networks of higher risk gamblers indicates difficulties in reducing or removing these influences. The findings indicate limitations of individualised interventions, and instead highlight the important role of changing norms within society, which can be transmitted throughout these networks.

Introduction

Gambling is often promoted and experienced as a legitimate social leisure activity for adults, shared with friends and family. Gambling products and marketing incorporate and encourage interaction with others, including through social media, ostensibly promoting social connection as part of the gambling experience ( Gainsbury, Delfabbro, King, & Hing, 2016 ; O’Loughlin & Blaszczynski, 2018 ). Research has potentially identified negative effects of social influences on gambling behavior, particularly problematic gambling behavior ( Raymen & Smith, 2017 ; Shead, Derevensky, & Gupta, 2010 ; Zhai et al., 2017 ), including among youth ( Canale et al., 2016 ; Dowling et al., 2016 ; Kristiansen, Trabjerg, & Reith, 2015 ). Recent findings that, at a population level, most gambling harm is from low- and moderate-risk gamblers ( Browne et al., 2016 ), highlight the need to improve our understanding of the determinants of harmful gambling, including social influences.

The biopsychosocial model of health offers a comprehensive theory to explain the initiation and sustainment of gambling behavior ( Sharpe, 2002 ) and recognize both unique and combined influences ( Abbott et al., 2015 ; Blaszczynski & Nower, 2002 ). On a biological level, these influences include genetic predisposition ( Williams, West, & Simpson, 2012 ) and gender ( Johansson, Grant, Kim, Odlaug, & Götestam, 2009 ). Psychological influences include personality ( Miller et al., 2013 ; Nower, Derevensky, & Gupta, 2004 ), motivation ( Gupta & Derevensky, 2000 ; Williams et al., 2012 ), reasoning ( Delfabbro & Thrupp, 2003 ), and mental health issues ( Blaszczynski, Russell, Gainsbury, & Hing, 2016 ). Examination of the social influences on gambling has largely focused on youth, particularly the active and passive influence of family and peers. Active influences occur when someone is encouraging, pressuring, or compelling someone to engage in a behavior such as gambling; whereas passive influences include processes of learned behavior, such as modeling and normalization ( Bandura, 1977 ; Bandura & Walters, 1963 ; Kandel & Andrews, 1987 ).

Early social influences on gambling behavior include perceived and actual family attitudes and behaviors toward gambling ( King & Delfabbro, 2016 ; Saugeres, Thomas, Moore, & Bates, 2012 ). The passive influence of modeling parental gambling behavior may begin early in childhood ( Pitt, Thomas, Bestman, Daube, & Derevensky, 2017 ), although the effects have mostly been measured in adolescents. Parental gambling behavior has been correlated with adolescents’ gambling attitudes and intention ( Pitt et al., 2017 ), as well as their gambling behavior ( Magoon & Ingersoll, 2006 ; Oei & Raylu, 2004 ; Wood & Griffiths, 1998 ). Of concern is where these influences have encouraged the early initiation of gambling during adolescence, which is a risk for future problematic gambling ( Dowling et al., 2017 ; Gay, Gill, & Corboy, 2016 ; Griffiths, 2010 ; Magoon & Ingersoll, 2006 ). The intergenerational transmission of problem gambling is related to the perceived financial and self-enhancing benefits of gambling ( Dowling et al., 2016 ). The relationship between parental gambling and subsequent problem gambling by the child is stronger when the parents themselves experience problems with gambling ( Dowling, Jackson, Thomas, & Frydenberg, 2010 ; Winters, Stinchfield, Botzet, & Anderson, 2002 ). Active social influences within the family include the facilitation of underage gambling by purchasing scratch cards and lotteries or placing sports bets ( Hardoon, Gupta, & Derevensky, 2004 ; Kristiansen et al., 2015 ; Reith & Dobbie, 2011 ), which serve to reinforce other passive influences of normalization.

Although family is the first, and often enduring social influence, as we age the social influence of family declines and that of friends and peers increases. Iterations of the reasoned action approach, such as the Theory of Planned Behavior and the Theory of Reasoned Action, explain the role of these social influences ( Ajzen, 1991 ; Fishbein & Ajzen, 1975 ). These approaches identify the role of intentions in driving behavior, with intentions being shaped by three factors: attitudes toward the behavior, perceptions of behavioral control, and normative beliefs (subjective norms). Subjective norms were defined by Ajzen ( 1991 , p. 188) as “ the perceived social pressure to perform or not to perform the behaviour ,” with the norm more likely to influence behavior if the individual is motivated to comply ( Oh & Hsu, 2001 ). For this reason, subjective norms are sometimes defined as what the individual thinks that “ important others believe the individual should do ” ( Finlay, Trafimow, & Moroi, 1999 , p. 2382). Notably, the individual’s perception of the norm does not have to be accurate for it to influence their behavior ( Cummings & Corney, 1987 ). Reasoned action approaches have been successfully applied to understanding gambling behavior and have identified the role of subjective norms as predictors of intention and, indirectly, behavior ( Dahl, Tagler, & Hohman, 2018 ; Larimer & Neighbors, 2003 ; Martin et al., 2010 ; Moore & Ohtsuka, 1999 ; Neighbors et al., 2007 ).

Research has identified social influences from both family and friends as influential in the initiation of gambling ( Kristiansen et al., 2015 ; Reith & Dobbie, 2011 ). However, the causal direction between social influences and the perpetuation or escalation of gambling is less clear. Do people become more like their existing social contacts over time (social influence)? Or do new or changing interests shape social connections, through selection of new contacts who share those interests, and disconnection from those who do not (social selection)? Compared to adolescents who gamble recreationally, those experiencing problems with gambling are more likely to have friends who also do ( Dickson, Derevensky, & Gupta, 2008 ) and will have lost friends who do not gamble through social deselection ( Gupta & Derevensky, 2000 ). Studies of adult males have identified similar patterns, with gambling an important part of the relationship among friendship groups, and facilitating social interaction with those outside the group when gambling interests aligned ( Gordon, Gurrieri, & Chapman, 2015 ; Raymen & Smith, 2017 ). Gambling can maintain a sense of belonging to a social community through a “ symbolic activity that represents and reaffirms group values ” ( Kristiansen et al., 2015 , p. 144).

Previous studies examining the relationship between social influences and gambling behavior have used correlational methods focused on the individual. Other public health research has demonstrated the value of examining social influence by broadening the analysis to social networks. Social network analysis (SNA) has examined both positive and negative impacts of social influence on behaviors or conditions, such as smoking ( Christakis & Fowler, 2008 ), obesity ( Christakis & Fowler, 2007 ), alcohol consumption ( Rosenquist, Murabito, Fowler, & Christakis, 2010 ), and depression ( Rosenquist, Fowler, & Christakis, 2011 ).

Only two studies have applied SNA to gambling behavior, both using egocentric SNA ( Meisel, Clifton, MacKillop, & Goodie, 2015 ; Meisel et al., 2013 ). Egocentric SNA specifically studies the social networks of individuals rather than those of a population as a whole (sociocentric SNA). In an initial small study ( N  = 40), Meisel et al. ( 2013 ) sampled pathological and non-pathological gamblers, finding that social networks around pathological gamblers included more people who gamble. They argued that this was due to people’s preference to associate with those who are similar, known as homophily. Although they found compositional differences between the social networks of pathological and non-pathological gamblers, they did not find structural differences. In a second study of 287 undergraduate students from a Southern American university, also using egocentric SNA, they extended the analysis to include other addictive behavior, such as smoking, drinking, and use of marijuana, finding similar patterns of clustered behaviors ( Meisel et al., 2015 ). Gambling behavior in this study was measured by frequency rather than a more traditional measure of risk such as the Problem Gambling Severity Index (PGSI; Ferris & Wynne, 2001 ).

To study the social networks of gamblers at different levels of problem gambling severity, and to overcome the limitations of previous small sample sizes, this study utilized egocentric SNA using a large ( N  = 784) adult sample of respondents. Specifically, the study aimed to examine how the role of social influences varies among different gambler risk groups by comparing key characteristics of their social networks.

This study was conducted in Victoria, Australia. Gambling is recognized as a legitimate leisure activity in Australia, with an annual participation rate approximating 64% ( Hing et al., 2014 ). About 7.9% of Australians experience some gambling-related problems, with 1.1% being classified as problem gamblers ( Armstrong & Carroll, 2017 ). In Victoria, the participation rate is approximately 70%, with 10.4% experiencing some gambling-related problems, and 0.7% classified as problem gamblers ( Schottler Consulting, 2015 ).

Respondents

A sample of 784 Victorian adults from a commercial panel provider participated in an online survey in late 2017. Respondents were presented with an information page outlining that the study was a social network study with questions about gambling, so that they could provide informed consent. A total of 2,024 potential respondents started the survey, but 548 were excluded for not meeting inclusion criteria (aged 18+ with no maximum age, living in Victoria, consenting to take part, committing to providing their best answers), and a further 100 were excluded for providing poor quality data (failed attention checks). A further 592 respondents started but did not complete the survey. Thus the completion rate, based on eligible respondents, was 784/(592 + 784) × 100 = 57.0%. Median completion time was 28.8 min, and respondents were compensated based on the standard practice of the research panels from which they were recruited.

The mean age of respondents was 35.3 years ( SD  = 14.5, range: 18–77) and 54.2% of respondents were female. Quotas were set, so that respondents were approximately evenly split between non-gamblers, and each of the four PGSI groups, facilitating statistical comparisons between all groups.

Procedure and measures

Egocentric SNA considers four main families of variables: measures about the ego, measures about the alters, ego–alter relationships, and alter–alter relationships. The latter are particularly important for deriving social network structure variables, as described below.

Ego measures

The respondents (henceforth egos ) provided information about their own demographics (age, gender, main language spoken at home, country of birth, highest level of education, number of dependent children living with them, work status, income, and disposable income). They were also asked about their gambling behavior (frequency over the last 12 months on each of nine forms of gambling, expenditure on each form) and problem gambling severity (the PGSI; Ferris & Wynne, 2001 ). We used the original PGSI cutoff scores (0 = non-problem gambler, 1–2 = low-risk gambler, 3–7 = moderate-risk gambler, 8+ = problem gambler), and Cronbach’s α in this sample was .94.

Alter measures

Egos were then asked about the 20 adults who they considered had been the most influential in their lives over the past 12 months (henceforth alters ). Previous social network analyses have used between 3 ( Wang & Muessig, 2017 ) and 30 ( Meisel et al., 2013 ) alters, and we found 20 alters to be an acceptable compromise in terms of information detail and survey length. Egos gave the name (or nickname) of each of the 20 alters, which was required for subsequent questions. They then reported their perceptions of the gambling behavior of each alter (frequency of engagement in gambling forms other than lottery games, instant scratch tickets and bingo in the past 12 months; which form they engaged in most) and level of harm due to gambling each alter had experienced over the past 12 months (no, minor, moderate, or severe harm).

Ego–alter relationship measures

The ego indicated the nature of their relationship with each alter (e.g., mother, father, brother, sister, other family member, friend, colleague, etc; recoded into family, friends, and colleagues), and how close the ego was to each alter (not particularly close, somewhat close, very close, and extremely close). The closeness measures were used to calculate a mean “tie to alter” strength. For alters who gambled, egos were asked to report how often the ego and each alter gambled together.

Alter–alter relationships

The ego then reported how close each pair of alters were (e.g., “How close is John to Peter?”) using the names provided for each alter, and using the same closeness scale as for ego–alter relationships. Initial analyses treated these closeness variables as a scale, as well as a dichotomy, where a relationship (“tie”) was considered to be present if it was rated as somewhat, very, or extremely close. Results were generally in alignment, so we have opted to report the latter recoding here.

Social network measures and statistical analysis

Ego measures (demographics, gambling behavior, and risk) were compared using standard statistical analyses across PGSI groups. These comprised analysis of variance (ANOVA) with Tukey’s pairwise comparisons, or non-parametric (Welch tests) where appropriate, for continuous measures; Kruskal–Wallis and Bonferroni-corrected Mann–Whitney U tests for ordinal measures and chi-square with pairwise tests of independence for categorical variables. Alter characteristics were calculated based on similarity to the ego. The absolute difference in age between the ego and each of their alters was calculated, and then averaged for a single mean absolute difference score per ego. Gender similarity, gambler status, and gambling-related harm status were calculated using Krackhardt and Stern’s ( 1988 ) EI index in E-Net v0.41 (Lexington, Kentucky; Borgatti, 2006 ), with negative scores indicating that more alters are of the same group as the ego (i.e., same gender, gambler status, or gambling-related harm status), and positive scores indicating more alters are in the opposite group to the ego. Alter–alter similarity is henceforth referred to as heterogeneity , calculated using Blau’s H, also in E-Net, with scores closer to 0 indicating that the alters in an ego’s network are mostly similar to each other on the relevant measure (homogeneity), whereas scores closer to 1 indicate that the alters are mostly different to each other (heterogeneity).

Finally, data from the alter–alter relationship measures were used to calculate the structural characteristics of each ego’s egocentric social network. Density refers to the proportion of possible alter–alter ties that actually exist. In the present data set, there were 190 possible alter–alter ties per ego, so, for example, in an ego’s network where 38 of the alter–alter relationships existed (i.e., a tie was considered to be present), density would be 38/190 = 0.20. Constraint is another measure of how interconnected an ego’s alters are. Here, if an ego is connected to alters who are highly connected to each other, then an ego is said to be constrained, with higher scores indicating higher constraint. Hierarchy is a measure of the nature of this constraint. If an ego is closely connected to a small number of alters who essentially act as the ego’s gateway to a larger social network, then hierarchy is higher. Once again, these measures are calculated as a score for individual egos, and can be compared using standard statistical analysis (ANOVA or equivalent non-parametric tests).

Analyses were conducted using a combination of SPSS v25.0 (Armonk, NY; IBM Corp., 2017 ) for descriptive and inferential statistics, E-Net v0.41 ( Borgatti, 2006 ) for calculating network structural variables, and R v3.4.0 (Vienna, Austria; R Core Team, 2017 ), with the following packages in particular: igraph, network, sna, ndtv, visNetwork, all for creating network diagrams.

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. All participants gave informed consent and were informed they could withdraw at any time. Data were anonymized before analysis. The study was approved by CQUniversity Australia Human Research Ethics Committee, clearance number H17/05-080.

Ego characteristics

As indicated in Table  1 , respondents in the problem gambler group were significantly more likely to be male compared to all other PGSI groups apart from moderate-risk gamblers. Non-problem gamblers were significantly more likely to be female compared to moderate risk and problem gamblers. Those in the non-gambler and the low risk, moderate risk or problem gambler groups were significantly younger compared to non-problem gamblers, and low-risk gamblers were significantly older than non-gamblers. Gamblers in the three highest risk groups were significantly more likely to speak English as their main language at home compared to non-gamblers, and all gamblers had a significantly higher income compared to non-gamblers, with problem gamblers having a higher income than non-problem gamblers. Those in the moderate risk or problem gambler groups were significantly more likely to be employed, particularly compared to non-gamblers and non-problem gamblers.

Ego demographic characteristics, and ego–alter similarity in terms of demographics, by ego group

Note. Pairwise comparisons are indicated by superscript letters (c, d, and e), indicating significant differences. In cases where a group has multiple superscripts, the group is not significantly different to any other group with either of those superscripts. For example, for gender, non-gamblers have a superscript of cd, and thus do not differ significantly from any other group with either c or d in their superscript (i.e., they only differ significantly from the problem gambler). No superscripts are shown when no significant differences were observed.

a Egos were given the option of “Other (please specify)” for gender, but none selected the option. b Egos were given the options “don’t know” and “prefer not to say.” Egos that selected those options were removed from analysis ( n  = 77).

Ego–alter similarity

No significant differences were observed between the groups in terms of the proportion of alters who were the same gender as the ego. However, the alters of egos in higher risk groups (moderate risk and problem gamblers) were significantly closer in age to the ego compared to non-problem gamblers.

Alter gambling behavior and gambling-related harm

The proportion of alters who gamble varied by the ego’s risk group. For non-gamblers, 3.72 of their 20 alters (on average) were gamblers, and this number increased through the PGSI risk groups. For egos classified as problem gamblers, 13.01 of their 20 alters on average were gamblers (Table  2 ). Alters who gamble were not more likely to come from any particular relationship group to the ego (family, friends, and colleagues) for any of the ego risk groups.

Mean ( SD ) number of alters who gamble, and who experience gambling-related harm, by ego group

Note. Pairwise comparisons are indicated by superscripts, with different letters indicating significant differences. In cases where a group has multiple superscripts, the group is not significantly different to any other group with either of those superscripts.

Not only did egos in higher risk groups associate with more alters who gamble, they also reported that most of their alters who gamble experience gambling-related harm. For egos classified as problem gamblers, approximately 60% of their alters who gamble were reported as experiencing gambling-related harm. Surprisingly, however, non-gamblers reported that a higher proportion of their alters experience gambling-related harm compared to non-problem gamblers (36.0% vs. 8.9%). No significant differences were found between relationship groups in terms of harmed alters within risk group, most likely due to inflated variance and reduced sample size (not all egos had alters in all three relationship groups).

Furthermore, gamblers in higher risk groups were significantly more likely to gamble with a higher proportion of their alters who gamble – up to 80% of them for egos classified as problem gamblers. Those in higher risk groups were also significantly more likely to gamble with alters who experience gambling-related harm.

Ego–alter relationship strength and structural network measures

Egos in low risk and problem gambler groups reported a stronger mean ego–alter relationship strength compared to non-problem gamblers. Similar relationships were observed for alters when considered by relationship type (family, friends, and colleagues), and also based on whether the alter was a gambler or non-gambler (Table  3 ).

Mean ( SD ) ego–alter relationship strength by alter subgroups and by ego group

Finally, the network structural measures indicated that the networks of those in higher risk groups, particularly problem gamblers, were significantly more dense (i.e., more alters were connected to each other) compared to non-problem gamblers. Problem gamblers were significantly more constrained than non-problem gamblers, and hierarchy was significantly lower for problem gamblers compared to all other groups apart from moderate risk gamblers (Table  4 ).

Mean ( SD ) network structural measures by ego’s group

Social network diagrams for egos classified as non-problem and problem gamblers

Example social network diagrams are shown for particular egos who were classified as a non-problem gambler (Figure  1 ) and a problem gambler (Figure  2 ). These egos were chosen to be representative of the sample based on the basis of the number of alters who gamble in their network, and the network density. For clarity, the ego is not represented in this diagram as they would be another circle connected to all of the alters.

The egocentric social network for an ego who is classified as a non-problem gambler. Note. Large circles are alters who gamble, and small circles are alters who do not gamble

The egocentric social network for an ego who is classified as a problem gambler. Note. Large circles are alters who gamble, and small circles are alters who do not gamble. Squares indicate alters who gamble and who have experienced gambling-related harm

In Figure  1 , distinct social groups are evident, with little interconnection between them. Most alters do not have a relationship with most of the other alters, and thus network density is low. In contrast, Figure  2 represents a highly connected (dense) network for an ego classified as a problem gambler. Their alters who gamble generally have relationships with each other, and appear to come from all relationship groups (i.e., the ego’s family, friends, and colleagues).

The results of this study paint a picture of those in higher risk groups, particularly problem gamblers, being surrounded by other gamblers. From the present cross-sectional study, it is difficult to determine whether these social influences drive gambling behavior (social influence), or whether the gambling behavior influences whom the ego associates with (social selection). Determining this distinction is likely to require longitudinal methodologies. No matter how this state arises, research has consistently found that social/peer norms influence gambling behavior ( Dahl et al., 2018 ; Larimer & Neighbors, 2003 ; Martin et al., 2010 ; Moore & Ohtsuka, 1999 ; Neighbors et al., 2007 ), leading to the conclusion that being surrounded by gamblers normalizes gambling behavior. Because 39% of Australian adults gamble at least monthly ( Armstrong & Carroll, 2017 ), gambling has become a normal activity. Despite this, there are high levels of community concern about normalization of a behavior like gambling (e.g.,  AdStandards, 2018 ), especially among children, adolescents, and young adults ( Thomas et al., 2018 ). This study has sought to understand how social networks may contribute to and help to maintain this normalization.

Not only are egos in higher risk groups surrounded by more gamblers, but also by more gamblers who experience gambling-related harm. If we accept the proposition that being surrounded by more gamblers normalizes gambling, then being surrounded by more gamblers who have experienced harm must normalize gambling-related harm. This influence is likely to be compounded for those in higher risk groups, because they gamble with a higher proportion of their alters who gamble, including those who experience gambling-related harm, and thus observing their behavior first hand may further normalize gambling ( Cullum, O’Grady, Armeli, & Tennen, 2012 ) and gambling-related harm. Thus, reducing the social influence of alters who experience gambling-related harm represents an important intervention for egos in higher risk groups. One alternate interpretation is that those in higher risk groups may have reported that most of their alters experienced gambling-related harm in order to feel better about their own harm. Future research could potentially include directly surveying alters to compare their harm level to that reported by the ego. In addition, the finding that non-gamblers report that a higher proportion of their alters who gamble experience harm (compared to the proportion reported by non-problem gamblers) is interesting, and may indicate that people who do not gamble have a lower threshold for what they consider constitutes gambling-related harm.

However, as indicated by the network density of egos in higher risk groups, reducing these influences by associating less, or not at all, with these alters may be difficult, because the social networks are so highly interconnected. We note this network density as a novel finding in gambling social network studies, as Meisel et al. ( 2013 ) did not find a difference in density, although this may be due to a lack of power due to their sample size. The finding of network density is, however, in agreement with the literature for other health behaviors, such as smoking ( Christakis & Fowler, 2008 ; Cutler & Glaeser, 2007 ; Ennett et al., 2008 ; Etcheverry & Agnew, 2008 ), alcohol consumption ( Abar & Maggs, 2010 ; Ali & Dwyer, 2010 ; Knecht, Burk, Weesie, & Steglich, 2011 ; Meisel et al., 2015 ; Mundt, 2011 ; Rosenquist et al., 2010 ; Stock et al., 2014 ), substance use ( Ennett et al., 2006 ; Pearson et al., 2006 ), and dietary patterns ( Christakis & Fowler, 2007 ; Pachucki, Jacques, & Christakis, 2011 ). Importantly, because gambling and gambling-related harm are pervasive through their social networks, there is an interactive or amplifying effect ( Epstein, Griffin, & Botvin, 2008 ; Mrug & McCay, 2013 ). Furthermore, the networks are not hierarchical (i.e., there is not a key connection that forms a critical path connecting the ego to their network), highlighting the challenges of reducing or eliminating these social influences for anyone in the network wishing to change their behavior.

A path for future research is to determine appropriate, and achievable, methods to reduce these harmful social influences. Because of this network density, approaches that facilitate an ego’s ability to be aware of and appropriately respond to these influences, rather than trying to eliminate them, may be more successful. However, the strength of these social bonds also forms an opportunity for behavior change and normalizing harm-minimization strategies and approaches that have been successful in other behaviors, such as smoking ( Christakis & Fowler, 2008 ), exercise ( Sandon, 2016 ), and bullying ( Wölfer & Scheithauer, 2014 ). Lessons can be learned from successful approaches in smoking reduction, which include increased taxes, reduction of locations where smoking is permitted, and increased education about harmful effects ( Cohen, Scribner, & Farley, 2000 ; Levy, Hyland, Higbee, Remer, & Compton, 2007 ). Peer influence can also be used to shape behavior, such as through normative feedback, which is designed to correct misperceptions by providing information about a person’s behavior compared to others of similar ages ( Moreira, Oskrochi, & Foxcroft, 2012 ). This feedback can be an effective tool for reduction of gambling behavior ( Auer & Griffiths, 2015 ; Celio & Lisman, 2014 ; Neighbors et al., 2015 ).

Limitations and strengths

Because the study was cross-sectional, it was not possible to determine the relative roles of social selection versus social influence. A longitudinal study, which allows for observations of change within the networks, is necessary to help determine their relative roles. A longitudinal study would also allow for the examination of change in gambling patterns over time, as these are not stable ( Delfabbro, King, & Griffiths, 2014 ), and help determine how changes in behavior relate to changes in social networks. Another limitation is that egocentric SNA, by design, does not require information from the alters, and thus the information about these alters comes solely from the ego. However, previous studies (e.g.,  Larimer & Neighbors, 2003 ) have found that the respondents’ beliefs about social norms are important predictors, which may somewhat mitigate this limitation. Furthermore, the measure of harm for each alter was short, because asking egos to fill in a validated harms screen for each of their 20 alters was not possible within the confines of the survey. In addition, because respondents were recruited using quotas based on non-gambler status or PGSI group status, the overall sample is not representative of the Victorian population. However, the purpose of the study was to compare networks across these groups, and thus the sample was not designed to be representative of the Victorian population as a whole. Finally, these social networks are, by definition, networks of individuals. Broader sociocentric SNA may be able to identify clusters of gamblers within a population, although sociocentric SNA has its own limitations (e.g., refusal and lack of anonymity) and may be impractical for behaviors such as gambling where public data about individuals, and their connections to each other, are not readily available.

The overall strengths of the study are that this is the first large-scale study to examine social networks in gambling by risk group. It is the first (to our knowledge) study to suggest that gambling harm may be normalized, and the first to identify challenges in altering social networks of gamblers in higher risk groups due to their degree of interconnectedness.

Conclusions

Many forms of gambling represent an important social relationship between people. Influences from people within a person’s social network can shape their gambling behavior through normalization, and for those in higher risk networks, also normalize gambling-related harm. The density of these networks, particularly for those in higher risk groups, presents challenges for those wishing to change their gambling behavior and for the reduction of gambling harm more broadly in the population. Instead, changing norms within society, which can in turn be transmitted through these networks, is likely to be more effective than changing the networks of individuals.

Acknowledgements

The authors would like to thank Mr. Vijay Rawat for his research assistance during this project. They would also like to thank the respondents for taking part in this research.

Funding Statement

Funding sources: This study was funded by the Victorian Responsible Gambling Foundation as an Early Career Researcher grant in Round 8 of their Grants for Gambling Research Program. The authors would like to thank them for their support of this project, particularly Sean O’Rourke.

Authors’ contribution

AMTR, EL, and NH designed the study, the survey, and conducted data collection. AMTR led the overall project, wrote the first draft of all sections of the manuscript, and conducted the statistical analyses. All authors read and commented on the manuscript and approved it for submission.

Conflict of interest

AMTR has received funding from Victorian Responsible Gambling Foundation, Queensland Justice and Attorney-General, Gambling Research Australia, National Association for Gambling Studies, Australian Communications and Media Authority, and the Alberta Gambling Research Institute. He has received industry funding for an evaluation of problem gambling among casino employees from Echo/Star Entertainment Group. He had travel expenses paid to present research by the Victorian Responsible Gambling Foundation. He is also affiliated to the University of Sydney. He declares no conflict of interest in relation to this manuscript. EL has received research funds from the Victorian Responsible Gambling Foundation, the Department of Families, Housing, Community Services and Indigenous Affairs (FaHCSIA), Gambling Research Australia, Department of Human Services, Education Queensland, Australia’s National Research Organisation for Women’s Safety, Lowitja Institute, and the New Zealand Ministry of Health. She has received an honorarium from Gambling Research Exchange Ontario, and had travel expenses paid to present her research by Gamble Aware, Gambling Research Exchange Ontario, Victorian Responsible Gambling Foundation, and the Gambling Impact Society. She declares that she has no conflicts of interest in relation to this manuscript. NH has received research funds from the Victorian Responsible Gambling Foundation, Gambling Research Australia, Australian Government Department of Social Services, Alberta Gambling Research Institute, the Australian Gambling Research Centre, the Queensland, New South Wales, Victorian and South Australian Governments, the Australian Research Council, and Australia’s National Research Organization for Women’s Safety. She has also received consultancy funds from Echo Entertainment and Sportsbet and an honorarium from Singapore Pools for membership of its International Advisory Committee. She declares that she has no conflicts of interest in relation to this manuscript.

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BRIEF RESEARCH REPORT article

Gambling behavior and risk factors in preadolescent students: a cross sectional study.

• Department of Psychology, Niccolò Cusano University, Rome, Italy

Although gambling was initially characterized as a specific phenomenon of adulthood, the progressive lowering of the age of onset, combined with earlier and increased access to the game, led researchers to study the younger population as well. According to the literature, those who develop a gambling addiction in adulthood begin to play significantly before than those who play without developing a real disorder. In this perspective, the main hypothesis of the study was that the phenomenon of gambling behavior in this younger population is already associated with specific characteristics that could lead to identify risk factors. In this paper, are reported the results of an exploratory survey on an Italian sample of 2,734 preadolescents, aged between 11 and 14 years, who replied to a self-report structured questionnaire developed ad hoc . Firstly, data analysis highlighted an association between the gambling behavior and individual or ecological factors, as well as a statistically significant difference in the perception of gambling between preadolescent, who play games of chance, and the others. Similarly, the binomial logistic regression performed to ascertain the effects of seven key variables on the likelihood that participants gambled with money showed a statistically significant effect for six of them. The relevant findings of this first study address a literature gap and suggest the need to investigate the preadolescent as a cohort in which it identifies predictive factors of gambling behavior in order to design effective and structured preventive interventions.

Introduction

In recent years, addiction has undergone changes both in terms of choice of the so-called substance and for the age groups involved ( Echeburúa and de Corral Gargallo, 1999 ; Griffiths, 2000 ). Although addiction is a condition associated to substance abuse disorder, it also determines other conducts that can significantly affect the lifestyle of subjects ( Schulte and Hser, 2013 ).

In the last edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) ( American Psychiatric Association, 2013 ), the pathological gambling behavior has been conceptualized differently than in previous editions, as a result of a series of empirical evidence indicating the commonality of some clinical and neurobiological correlates between pathological gambling and substance use disorders ( Rash et al., 2016 ). The new classification into the “ Substance-Related and Addictive Disorders ” category supports the model of behavioral addictions in which people may be compulsively and dysfunctionally engaged in behaviors that do not involve exogenous drug administration, and these conducts can be conceptualized within an addiction framework as different expressions of the same underlying syndrome ( Shaffer et al., 2004 ).

Despite the fact that in many countries gambling is forbidden to minors, in recent years, there has been a marked increase in this behavior among younger people so that from surveys conducted in different cultural contexts it emerges that a percentage between 60 and 99% of boys and between 12 and 20 years have gambled at least once ( Splevins et al., 2010 ). The increasing number of children and underaged youth participating in games of chance for recreation and entertainment is attributable to the legalization, normalization, and proliferation of gambling opportunities/activities ( Hurt et al., 2008 ).

Several studies have shown that the percentage of young people who gamble in a pathological way is significant and even greater than the percentage of adult pathological gamblers ( Blinn-Pike et al., 2010 ). Using the definitions of at-risk and problem gambler that directly refer to the diagnostic criteria for pathological gambling, the review of Splevins et al. (2010) showed that a percentage of adolescents between 2 and 9% can be classified within the category of problem gamblers, while between 10 and 18% are adolescents who can be considered at-risk gamblers.

The first comprehensive review on problematic gambling in Italy noted a lack of large-scale epidemiological studies and of a national observatory regarding this issue ( Croce et al., 2009 ). More recent studies regarding the Italian national context are now available. A survey carried out with 2,853 students aged between 13 and 20 years showed that 7% of adolescents interviewed were classified as pathological gamblers ( Villella et al., 2011 ), while the study conducted by Donati et al. (2013) indicated that 17% of adolescents showed problematic gambling behaviors.

As far as ecological factors are concerned, the crucial role of family and play behavior of friends has been widely documented. In particular, a strong association between parents’ and children’s gambling behavior has emerged ( Hardoon et al., 2004 ), and it has been highlighted that the spread of gambling in the group of friends influences the practice of gambling among adolescents ( Gupta and Derevensky, 1998 ).

Traditionally, gambling in youth was considered as related to poor academic achievement, truancy, criminal involvement, and delinquency. More recently, investigators have examined the relationship between gambling and delinquent behaviors among adolescents in a systematic way, shifting the understanding beyond the explanation that delinquency associated with problem gambling is merely financially motivated by gambling losses ( Kryszajtys et al., 2018 ). This suggests that young players may have more general problems of conduct than specific criminal behavior.

Conversely, in relation to poor academic achievement, it has been highlighted that problem gambling in adolescence affect students’ performance mainly by reducing the time spent in studying ( Allami et al., 2018 ).

Although the phenomenon of gambling has been widely analyzed in the adult population and there are numerous studies on the adolescent population, the data in the literature suggest that gambling may be a phenomenon already present in preadolescence and needs to be analyzed. In fact, the lowering of the age of onset of problematic behaviors related to pathological gambling raises a question about the presence of gambling in preadolescents, as more exposed to the use of the Internet, smartphones, and tablets as tools that could encourage this type of conduct. A series of studies ( Shaffer and Hall, 2001 ; Vitaro et al., 2004 ; Winters et al., 2005 ; Kessler et al., 2008 ) have highlighted how adult pathological players started playing significantly earlier from a non-pathological player’s chronological point of view.

Nevertheless, it has been seen in the literature as, within the population of those who start playing before the age of 15, only 25% maintain the same frequency of play even in adulthood ( Vitaro et al., 2004 ; Delfabbro et al., 2009 , 2014 ).

In the review by Volberg and colleagues, it was shown how teenagers tend to prefer social and intimate games, such as card games and sports betting, while only a small percentage of teenagers are involved in illegal age gambling activities ( Volberg et al., 2010 ).

Pathological and problem players seem to be more involved in machine gambling (such as slot machines and poker machines), non-strategy games (such as bingo and lottery or super jackpot), and online games; they play in different contexts such as the Internet, school, and dedicated rooms ( Rahman et al., 2012 ; Yip et al., 2015 ).

It has been seen that online gambling is particularly attractive for young people due to its extreme accessibility, the large number of events dedicated to gambling, accessibility from the point of view of the economic share invested, and the multisensory experience and high level of involvement reported by young people ( Brezing et al., 2010 ; King et al., 2010 ).

Considering what is present in the literature, it is evident that the phenomenon of pathological gambling in adulthood is linked to a series of risk factors already present in adolescence. At the same time, the progressive lowering of the age at the beginning, which has been seen to be one of the main risk factors, makes it necessary to analyze the presence of the phenomenon of gambling in preadolescents, an analysis that at this time cannot count on the support of validated tools and questionnaires.

Considering that young people spend part of their time playing, it is necessary to distinguish between what is considered a game and what is considered gambling, even if not in a pathological way.

According to King et al., “gaming is principally defined by its interactivity, skill-based play, and contextual indicators of progression and success. In contrast, gambling is defined by betting and wagering mechanics, predominantly chance-determined outcomes, and monetization features that involve risk and payout to the player” ( King et al., 2015 ).

Primarily, the objective of this study is to verify the presence, the possible extent, and the characteristics of the phenomenon of gambling as defined before in a population of preadolescents (percentage, distribution by gender) to see if the population of preadolescent players shows the same characteristics as those found in larger populations at the age level (adolescents and adults). Secondly, the study aims to verify any differences in the perception of the game between those who play and those who do not, in order to identify additional specific characteristics.

In addition, on the basis of what is highlighted in the literature with respect to the risk factors detected in adults and adolescents, the study aims to assess whether and which of these factors can be predictive of the phenomenon of preadolescent gambling.

Finally, always in line with the identification of possible prodromal factors of gambling, the study wants to analyze the differences with respect to the types of games preferred by preadolescent players to assess any similarity with what emerged in the adolescent population.

In addition, the study aims to verify whether preadolescent players show the same game-level preferences highlighted in the literature as risk factors for the development of a real game disorder ( Rahman et al., 2012 ; Yip et al., 2015 ).

Materials and Methods

The investigation followed the Ethical Standards of the 1994 Declaration of Helsinki, and the study was approved by the Departmental Research Authorization Committee of Niccolò Cusano University and the Italian Ministry of Labour and Social Policy. In a prospective study of gambling perception, behavior, and risk factors, youth aged 11 to 14 years were recruited from 47 schools situated in 18 regions of Italy. The respondents’ survey was composed by 2,734 preadolescents (1,256 female and 1,452 male), enrolled in the 6, 7, and 8 grades across all national areas (18 provinces out of 20 Italian regions).

The administration of the survey was approved by the school boards of all the institutes involved, and all parents signed the informed consent and authorization to process personal data of their children. The self-report questionnaire was proposed and filled out in the classroom during school time.

The complete questionnaire developed ad hoc by the authors for the survey is composed of 19 items, 6 related to demographic characteristics of the sample and the remaining tighter focused on gambling behaviors and information related to the context of the subject. An excerpt of all the analyzed questionnaire items is provided in the appendix to facilitate the understanding of the Likert scale administered (see Supplementary Data Sheet 4 ).

After data screening, which excluded incomplete/invalid questionnaires, the sample presented the following characteristics: gender, 1,312 male (53%) and 1,163 female (47%); nationality, 93% Italian and 7% others; age: M = 12.36, SD = 0.95, distributed in 11 years old n = 541 (21.9%), 12 years old n = 803 (32.4%), 13 years old n = 841 (34.0%), and 14 years old n = 290 (11.7%).

Gamblers were defined as individuals who showed gambling behaviors in the previous year, classified as the ones who answered “yes” to the question “In the last twelve months did you game and gamble money playing any game?”

In the first sets of analysis, data were examined to determine whether there was an association between the gambling behavior and individual or ecological factors measured on nominal, continuous, or ordinal scales. Variable dependence was assessed as appropriate using chi-square for nominal variables, t -test for comparing groups on two continuous variables (e.g., age), or the sound nonparametric Mann-Whitney U test to confront two ordinal variables (e.g., Likert 5/4-point scale from fully agree to fully disagree). The decision to apply nonparametric tests was made considering the correlational research design of the survey and the non-previously validated questionnaire as the tool for collecting data. Moreover, the utilization of nonparametric analysis gives the most accurate estimates of significance in case of non-normal data distributions and variables of intrinsic ordinal nature as the ones obtained from Likert items in the questionnaire ( Laake et al., 2015 ).

For the same reason, a Friedman test was run to determine if there were differences in the playing rates of gamers concerning different games of chance, because this nonparametric test determines if there are differences between more than two variables measured on ordinal scales, e.g., when the answers to the questionnaire items are a rank ( Conover, 1999 ). The different categories of game taken into account were “videopoker, slot machine e video slot,” “lotto, lottery and superjackpot,” “Scratch card,” “Sport bets,” and “Daily fantasy sports.”

The second set of analyses examined the probability of being in the category “gamblers” of the dependent variable given the set of relevant independent variables already identified in base of preliminary analysis results and substantive literature support. More specifically, the following variables measured by the questionnaire were analyzed: gender, inappropriate school behavior, parent with gambling behavior, and troubles with parent – videogame-related and gambling-related. In this perspective, model selection in the multivariate logistic regression is aimed to the understanding of possible causes, knowing that certain variables did not explain much of the variation in gambling could suggest that they are probably not important causes of the variation in predicted variable. Moreover, introduction of too many variables could not only violate the parsimony principle but also produce numerically unstable estimates due to overfitting ( Rothman et al., 2008 ).

Individual characteristics of participants who gambled (gamblers) versus participants who did not gamble (nongamblers) are shown in Supplementary Table S1 .

Gamblers were more likely males, older, and showed a higher record of inappropriate behavior at school in the past. Moreover, the parents of these students presented a higher proportion of gambling behavior and family conflicts related to playing videogames or gambling. As shown in Supplementary Table S2 , the two groups also differed significantly on the variable “online gambling without money.”

Subsequently, several Mann-Whitney U tests were run to determine if there were differences in the perception of many gambling’s facets (measured through self-report scores) between gamblers and nongamblers. To analyze the perception of the game and any differences between players and nonplayers have been isolated four variables measured through the following items: “loosing money because of gambling,” “becoming rich through gambling,” “gambling is funny,” “gambling is an exciting activity.” The distributions of the perception scores for gamers and not gamers on these four items were similar, as assessed by visual inspection. Median perception of gambling as a risk was statistically significantly lower in gamblers (3) than in nongamblers (4), U = 344, z = −4.59, p < 0.001, as well as the difference between median perception scores of gambling as an habit was statistically significantly lower in gamblers (3) than in nongamblers (4); U = 357, z = −3.48, p < 0.001. Statistically significant differences were also found between the median perception scores of gamblers and nongamblers on the variable “ losing money because of gambling ” [lower in gamblers (3) than in nongamblers (4); U = 327, z = −6.27, p < 0.001] and “ becoming rich through gambling ” [higher in gamblers (2) than in nongamblers (1); U = 519, z = 9.879, p < 0.001].

Differently, on two similar items regarding the perception of gambling as an entertaining activity and as an exciting activity, the distributions for gamblers and nongamblers were not similar, as assessed by visual inspection. One of the two items concerned the perception of gambling as an entertaining activity; the Mann-Whitney U test revealed that scores for gamblers (mean rank = 1.8) were significantly higher than for nongamblers (mean rank = 1.14; U = 608, z = 17.52, p < 0.001). The last item concerned the perception of gambling as an exciting activity; the Mann-Whitney U test revealed that scores for gamblers (mean rank = 1.7) were significantly higher than for nongamblers (mean rank = 1.16; U = 569, z = 14.23, p < 0.001).

For this reason, a Friedman test was run to determine if there were differences in the playing rates of gamers concerning different games of chance, because this nonparametric test determine if there are differences between more than two variables measured on ordinal scale, i.e., when the answers to the questionnaire items are a rank ( Conover, 1999 ). The students who stated to have gambled money in the previous 12 months were asked in the following question about the frequency they played different group of games.

Pairwise comparisons were performed ( IBM Corporation Released, 2017 ) with a Bonferroni correction for multiple comparisons. Gambling/playing rate was statistically significantly different in the five groups of games, χ 2 (4) = 226.693, p < 0.0005. The values of post hoc analysis are presented in Supplementary Table S2 , and the Pairwise Friedman’s comparisons revealed relevant statistically significant differences in playing rates of gamers. In fact, the category of game of chance constituted by “videopoker, slot machine e video slot” (mean rank = 2.46) is preferred to all other kinds of game of chance, except “lotto, lottery and superjackpot” (mean rank = 2.50). In the case of “Lotto, lottery, SuperJackpot,” this category of game of chance is preferred to “Scratch card” (mean rank = 3.30) in a statistically significant way, but it is also statistically less played in comparison to “Sport bets” (mean rank = 3.35) and “Daily fantasy sports” (mean rank = 3.40). None of the remaining differences were statistically significant.

Regarding the second set of analyses, Supplementary Table S3 provides the model used in the binomial logistic regression performed to ascertain the effects of key variables on the likelihood that participants played game of chance with money. The logistic regression model was statistically significant, χ 2 (7) = 326, p < 0.001. The model explained 23.0% (Nagelkerke R 2 ) of the variance in the predicted variable (gambling behavior) and demonstrated a percentage accuracy in classification (PAC) equal to 86.6%. Sensitivity was 22.5%, specificity was 97.6%, positive predictive value was 62.2%, and negative predictive value was 87.9%. Of the seven predictor variables only six were statistically significant: gender, inappropriate school behavior, parents with gambling behavior, troubles with parents – videogames related, online gambling without money, and age (as shown in Supplementary Table S3 ). Analysis showed that male had 2.96 times higher odds to be gamers than females (OR = 0.337; 95% CI 0.248–0.458), and increasing age was associated with an increased likelihood of gambling behavior. Also, inappropriate school behavior (OR = 1.859; 95% CI 1.395–2.477), parents with gambling behavior (OR = 3.836; 95% CI 2.871–5.125), troubles with parents – videogames related (OR = 1.285; 95% CI.510–3.236), and online gambling without money (OR = 2.297; 95% CI 1.681–3.139) increased the likelihood of gambling. By contrast, the “Troubles with parents – gambling related” variable was not statistically significant, probably because of the extremely unbalanced case ratio between the two modalities.

The first objective of this study was to evaluate the presence or absence and the consequent extent of the phenomenon of gambling in a population of preadolescents and to understand which factors are associated to the progressive lowering of the age of onset.

Consistently with the literature on the adult and adolescent population, the evidence presented thus far supports the idea that even in the preadolescent population players tend to be predominantly males ( Hurt et al., 2008 ; Splevins et al., 2010 ; Villella et al., 2011 ; Dowling et al., 2017 ).

One of the more significant findings to emerge from this study is that players of game of chance have a significantly different perception of the game than nonplayers, i.e., they see the game as “less risky” and perceive less risk of losing money through the game. In addition, confirming this “altered” perception, they show higher values than nonplayers in the perception of being able to become rich through the game ( Hurt et al., 2008 ; Dowling et al., 2017 ). Gamblers have a perception of the game as exciting and fun, a tendency which increases with age. This pattern seems to confirm what is expressed in the literature regarding the theme of sensation seeking and its connection with the development of gambling disease ( Dickson et al., 2002 , 2008 ; Hardoon and Derevensky, 2002 ; Messerlian et al., 2007 ; Blinn-Pike et al., 2010 ; Shead et al., 2010 ; Ariyabuddhiphongs, 2011 ; Lussier et al., 2014 ).

Even more importantly, some possible predictive factors of gambling emerged among the variables analyzed: thus, the phenomenon of gambling was associated with problems of school conduct, problems with parents related to the use of video games and, interestingly, also to the presence of parents who are gamers.

Since there are no validated tools in the literature for the diagnosis of preadolescent gambling, the analyses were conducted on those who were “gamblers” according to what was previously stated. It is therefore of particular relevance that the sample of preadolescent gamblers shows descriptive characteristics and predictive factors similar to those highlighted by the literature on adolescent gamblers with a diagnosis of gambling.

In this sense, the analysis of the most frequently used game types is particularly important.

With respect to the game categories analyzed, with the exception of “Lotto, lottery, SuperJackpot,” the category that is most frequently chosen by the sample of gamblers is that of “videopoker, slot machine e video slot.”

These data are of particular relevance considering that some studies in the literature have shown that adult pathological players have shown in previous ages a strong preference for these types of games. Although it is necessary to investigate with further studies the reasons underlying the choice of this type of game by preadolescents, this fact suggests that the phenomenon of preadolescent gambling has a number of aspects and characteristics common to those identified by the literature in the analysis of the precursors of pathological gambling.

There are some issues to take under consideration in framing the present results. Regarding the sample, although the numerous participants and the geographical representativeness of the population, the sample was not randomly selected. Therefore, we cannot exclude that subjects were unbalanced on unobserved, causally relevant concomitants. Although the methodology allows prediction, it should be noted that causality cannot be established from this survey, because the research design does not properly establish temporal sequence. In addition, only self-report measures and not thoroughly validated scales were used, as the objective of this study was to conduct an exploratory survey on the characteristics of the phenomenon, and there were some dichotomous variable with uneven case ratios. Furthermore, some constructs related to gambling behavior (e.g., impulsivity) and neurocognitive functioning were not analyzed in designing this first study; although in the wider research program, it is intended to explore also these factors.

Notwithstanding these limitations, the present study makes some noteworthy contributions to the understanding of the phenomenon of gambling and its characteristics in a population (preadolescents) which is still not very explored in the literature.

In particular, one significant finding is that the lowering of the age has not substantially changed what has been established in the literature with respect to the phenomenon in adolescents: the characteristics of players in terms of gender are substantially unchanged in the comparison between adolescents and preadolescents.

Moreover, from the analyses carried out, it appears that those that the literature has highlighted as risk factors of gambling in adolescence and adulthood are already present in younger players and may be predictive factors of gambling conduct already in preadolescence.

The data show, moreover, that the perception of gambling for those who play is significantly different from those who do not play, and specifically on aspects related to attractiveness, the low perception of risk and the possibility of getting rich easily. Finally, even with respect to an analysis carried out on different types of games, what emerged from the literature as additional risk factors for adolescents and adults is already present in preadolescence.

The findings of this study focus on the need to investigate the preadolescent age group in order to identify specific predictive factors of gambling in order to structure effective and structured preventive interventions and the parallel need to structure a standardized tool for the diagnosis of gambling in this specific population.

Data Availability

The datasets generated for this study are available on request to the corresponding author.

Ethics Statement

The study was carried out according to the principles of the 2012–2013 Helsinki Declaration. Written informed consent to participate in the study was obtained from the parents of all children. The study was approved by the IRB of the Department of Psychology of Niccolò Cusano University of Rome.

Author Contributions

NV and GF designed and performed the design of the study and conducted the literature searches. CD, MC, and GP provided the acquisition of the data, while FM undertook the statistical analyses. NV, CP, and FM wrote the first draft of the manuscript. All authors significantly participated in interpreting the results, revising the manuscript, and approved its final version.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/article/10.3389/fpsyg.2019.01287/full#supplementary-material

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Keywords: gambling, risk factors, preadolescence, addiction, prevention

Citation: Vegni N, Melchiori FM, D’Ardia C, Prestano C, Canu M, Piergiovanni G and Di Filippo G (2019) Gambling Behavior and Risk Factors in Preadolescent Students: A Cross Sectional Study. Front. Psychol . 10:1287. doi: 10.3389/fpsyg.2019.01287

Received: 15 February 2019; Accepted: 16 May 2019; Published: 12 June 2019.

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Copyright © 2019 Vegni, Melchiori, D’Ardia, Prestano, Canu, Piergiovanni and Di Filippo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Nicoletta Vegni, [email protected]

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• Research article
• Open access
• Published: 22 August 2017

Why do young adults gamble online? A qualitative study of motivations to transition from social casino games to online gambling

• Hyoun S. Kim 1 ,
• Michael J. A. Wohl 2 ,
• Rina Gupta 3 &
• Jeffrey L. Derevensky 4  

Asian Journal of Gambling Issues and Public Health volume  7 , Article number:  6 ( 2017 ) Cite this article

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The present research examined the mechanisms of initiating online gambling among young adults. Of particular interest was whether social casino gaming was noted as part of young adults’ experience with online gambling. This is because there is growing concern that social casino gaming may be a ‘gateway’ to online gambling. Three focus groups ( N  = 21) were conducted with young adult online gamblers from two large Canadian Universities. Participants noted the role of peer influence as well as incentives (e.g., sign up bonuses) as important factors that motivated them to start engaging in online gambling. Participants also noted a link between social casino games and online gambling. Specifically, several young adults reported migrating to online gambling within a relatively short period after engaging with social casino games. Potential mechanisms that may lead to the migration from social casino games to online gambling included the role of advertisements and the inflated pay out rates on these free to play gambling like games. The results suggest initiatives to prevent the development of disordered gambling should understand the potential of social casino gaming to act as a gateway to online gambling, especially amongst this vulnerable population.

Over the past decade, the use of computers and the Internet has significantly altered the gambling landscape. The gambling industry is no longer bound by brick and mortar gambling venues (e.g., casinos, racetracks). Today, access to gambling activities can be achieved with a few keystrokes on a computer. One point of access that has gained increased attention from researchers in the field of gambling studies is social media sites such as Facebook (Wohl et al. 2017 ). In part, this increased attention is because social media sites have become a popular platform for people to access online gambling venues via hyperlinks embedded in advertisements (Abarbanel et al. 2016 ). Social media sites also allow users to engage in free-to-play simulated gambling games through applications. These free-to-play simulated gambling games have become referred to as social casino games (Gainsbury et al. 2014 ). There is evidence to suggest, however, that social casino game play may act as a ‘gateway’ to gambling for real money (for a review see Wohl et al. 2017 ).

The current research took a qualitative approach to assess young adult online gamblers experiences with online gambling to determine the process and mechanisms that may lead young adults to gamble online, including the role of social casino games. In other words, the present research aimed to examine the motivations for gambling online, including transitioning from social casino games to online gambling. A focus was placed on young adults’ experience with online gambling due to their propensity to gamble online (McBride and Derevensky 2009 ), play social casino games (Derevensky and Gainsbury 2016 ), as well as their elevated rates of disordered gambling (Welte et al. 2011 ). Further, social casino games were the focus as there is a current need to understand the issues regarding the gaming-social media crossover.

Online gambling and social casino gambling among young adults

The Internet has changed the way people engage in many activities, including gambling. Online gambling (compared with land-based gambling) provides players with ease of access, 24/7 accessibility, and confidentiality—all within the comfort of a person’s home. This ease of access has been flagged as a potential concern among researchers, regulators, and policy makers alike (Gainsbury 2015 ; Gainsbury and Wood 2011 ; Räsänen et al. 2013 ). Specifically, online gambling is often framed as a ‘risky’ form of gambling that may heighten the risk of developing a gambling disorder (Gainsbury et al. 2015b ; Griffiths et al. 2009 ; McBride and Derevensky 2009 ; Olason et al. 2011 ; Wood et al. 2007 ). In this light, it may be informative to examine factors that propel young adults to gamble online, including the link between social casino gaming and online gambling. This is because there is increasing evidence of the role played by social casino games in precipitating online gambling (Wohl et al. 2017 ) and young adults are increasingly exposed to social casino games (Kim et al. 2016 ).

Social casino games are an immensely popular form of entertainment, with millions of users playing in any given day (Derevensky and Gainsbury 2016 ; Martin 2014 ). One reason for their popularity may be their ubiquity on social network sites like Facebook , which provide ample opportunities to play social casino games via embedded apps (Gainsbury et al. 2014 ). Moreover, social casino games are among the most heavily advertised products on social network sites and convey the activity (i.e., gambling) as positive and glamorous (Gainsbury et al. 2015a ). These advertisements appear to have a significant influence on engagement with social casino games (SuperData 2016 ). It should be noted that some social casino games are now owned by online gambling operators who advertise their online gambling site within the social casino game, thus easing migration from social casino gaming to online gambling (Schneider 2012 ).

There is now converging evidence that suggests social casino gamers migrate to online gambling (Gainsbury et al. 2016 ; Kim et al. 2015 ). Furthermore, amongst people who engage in both gambling and social casino gaming, social casino games directly increase future gambling behaviors (Gainsbury et al. 2016 , 2017 ; Hollingshead et al. 2016 ). Social casino games are also popular among adolescents and young adults. In a large Canadian survey of over 10,000 students, roughly 9% reported having played social casino games (Elton-Marshall et al. 2016 ). In addition, a recent longitudinal study in a large sample of adolescents found that social casino games significantly predicted the transition to real money gambling (Dussault et al., in press). Providing further support for the popularity of social casino games, in focus groups with university students who were social media users, all participants reported being aware of the ample opportunities to play social casino games on Facebook, thus speaking to the increased exposure of these games on social networking sites (Kim et al. 2016 ).

Motivations for transitioning to online gambling from social casino gaming

Social casino games are popular among adolescents and young adults and may influence the transition to online gambling. Yet, researchers have paid little attention to potential processes or mechanisms that influences the transition to online gambling amongst this cohort, including the role played by social casino games. With that said, Hollingshead et al. ( 2016 ) argued that the motivations for playing social casino games likely mimic those of online gambling, including for excitement, to relieve boredom, and social motivations. In addition, they reported that some social casino gamers are motivated to engage in these games to hone their skills before playing for real money on online gambling sites. In line with Hollingshead et al. ( 2016 ) and King and Delfabbro ( 2016 ) proposed a framework for understanding factors that may increase or decrease the link between social casino gaming and online gambling among adolescents. Specifically, in their two pathways model, they identify both protective (e.g., early losses, awareness of risks, boredom) and risk factors (e.g., peer pressure, early big wins, greater confidence of winning) that may lead adolescents who are exposed to social casino games to either be disinterested in gambling or to increase future gambling behaviors.

The present research sought to add to the growing literature on the potential link between social casino gaming and online gambling. To do so, focus groups with young adult online gamblers were conducted to explore their motivations for gambling online, including the potential role social casino games played in initiating or facilitating online gambling behaviors. Focus groups provide a compromise between obtaining personal experiences without having to interview people individually, while also having a group environment where other people’s experience stimulate the recall and views of others. In this light, focus groups are an effective method of obtaining a variety of detailed information in an exploratory way.

Participants

Twenty-one young adults (18 males, 3 females) were recruited from two large Canadian Universities to participate in one of three focus groups described as being about young adults’ experience with online gambling. Specifically, the study was advertised as a focus group for people who gambling online. It was explained that we were interested in online gamblers’ “opinions and experiences regarding online gambling”.

The inclusion criteria were as follows: college students aged 18–24 years who reported gambling online at least twice per month. The method of recruitment occurred in two ways. First, all incoming first year students at one of the large Canadian universities complete a short survey screening for disordered gambling. Embedded in that questionnaire were items that assessed online gambling. This allowed us to recruit participants who met the inclusion criteria for the focus groups. Only those who consented to be recruited for future studies were contacted. The second method of recruitment consisted of visiting large classrooms and advertising the study at both universities.

While every effort was made to recruit an equal number of male and female online gamblers we were unable to do so despite our best efforts. Moreover, seven individuals who had initially agreed to participate in the study subsequently notified the research team before the group meeting that they could not participate for logistical reasons (i.e., work and school commitments, unexpected appointments). Participants in the first group were compensated $20 for their time and those in the remaining two groups were provided with $40 (the increased compensation was used as an incentive to attract a greater number of participants and was cleared by the authors’ Research Ethics Board). Additionally, participants were provided food and beverages throughout the course of the discussions that ensued.

Procedure and materials

All participants were provided with a description of the study objectives and were asked to read and sign an informed consent prior to participating in the current research. Participants were informed they were free to terminate participation at any time without penalty. Thereafter, participants were asked to complete a short background questionnaire, which included demographic information (gender, age), frequency of gambling, and how knowledgeable they believe themselves to be on the topic of online gambling.

A series of open-ended questions were asked of the group as part of a larger project assessing online gambling among young adults. For the present research, two open-ended questions were of importance. The first examined general factors that lead young adults to gamble online, “I’d like to gain a better understanding of the things that lead to online gambling in the first place. Based on what you know, what are the factors, the events, or the influences that result in a young person deciding to bet money on gambling activities online?” The second assessed the social casino game-online gambling link including the potential mechanisms, “You know that social media sites have gambling-type games such as Texas-Hold’em or Sloto-mania. In your opinion, do you think experience with these games leads a person to seek online gambling sites? In other words, do these types of games serve as a form of initiation to gambling online with real money?”

A licensed clinical psychologist trained in conducting focus groups led the discussions accompanied by two note-takers. Each group was approximately 60–75 min in duration and discussions were conducted at two Canadian universities. Two recording devices recorded the focus group to ensure no loss of data. Upon the completion of the focus groups, the discussions were subsequently transcribed by a professional coder and coded by two independent reviewers. The initial categories generated by the data were highly consistent between the two raters with regards to general themes and number of categories. The data was reviewed two additional times to arrive at a consensus when disagreements between raters were noted. Categorical names were arrived through consensus after discussion between raters. NVivo 10 qualitative research software for qualitative analyses was used to organize and quantify the data.

With respect to frequency of online gambling, 52% of individuals indicated gambling less than once per week, while 48% indicated gambling at least once per week or several times per week. Seventy-six percent of individuals indicated gambling more frequently and/or for longer periods of time than intended (61.9% occasionally; 14.4% often). Participants were asked to indicate on a 7-point Likert scale how knowledgeable they perceived themselves to be on the topic of online gambling. The overall mean score was 4.38. The majority of the sample (85.7%) indicated that they tend to play on one or two online gambling sites, whereas 14.3% stated they like to experiment with different sites. Importantly, more than half (62%) of the participants revealed playing social casino games (e.g., Texas Hold’em ) on Facebook or on other platforms. Of the participants ( n  = 3) who spontaneously reported having transitioned from playing for fun to online gambling, they did so in relatively short period of time. One participant reported transitioning after only two weeks, while another stated having moved to real money gambling after a couple of months.

General factors leading to online gambling

Several themes emerged in regards to the factors that led the emerging adults to online gambling. For example, some of the emerging adults in the focus groups stated that friends played an important role in their initial participation to online gambling. Specifically, several participants reported having first learned to gamble with friends and thereafter transitioning to online gambling as their friends were not always available.

From my personal experience for example, I started gambling online with poker because I started playing poker with friends, and that is how I got to gambling online… with friends they did not always have the time [to play poker]. Gambling online was just easier – with friends they did not always have time.

Another theme that was noted in the precipitation of online gambling was the incentives (e.g., sign up bonuses) offered by online gambling. The young adult online gamblers noted that the first time they gambled online was when they were offered bonuses and free credits. Indeed, the participants agreed that the bonuses were an important incentive in moving to online gambling.

The bonuses actually attract us to them. You don’t get that at the casino.

For me the first incentive was they offered us 10 lb… so I got the 10 lb and then started betting real money

Motivations from transitioning from social casino games to online gambling

Texas Hold’em with free chips, that’s how I started. A general progression starts with these Facebook entertainment games which are purely for fun and some people take it to the next level where it’s for fun and money, that’s where we are now - most of us and then some people will take it eventually to the next level where the fun has disappeared and they are just doing it for the money.

Social casino games were noted as a potential factor that influenced the initiation of online gambling among young adults. In fact, whilst the moderator had intentions to bring up social casino games as a topic, in all three focus groups, the young adult online gamblers spontaneously brought up social casino games. These results indicate that social casino games are a salient aspect of young adult online gamblers’ experiences. Not surprisingly, the young adult online gamblers mentioned the constant advertisements as a potential factor that may lead social casino gamers to online gambling. Specifically, the frequent nature of the advertisements that provided social media users with an opportunity was brought up by several focus group members, with few young adult online gamblers mentioned the role of advertisement in the transition to online gambling.

I’d argue that you are just sort of lured into playing more through back link advertising where you will have all these ads like partypoker.com keep coming back at you even when you are on other sites…

… and obviously the companies [social media] give out the information on things that you are doing like all the games and poker, even though it’s not for money. Your side bar has all advertisements that are personalized to you so for me I see a lot of gambling, sports, apparel stuff and stuff like that is all on my side bar.

When I started, it was Facebook. Randomly the opportunity comes up with ads. I was stressed so I went to the online casino from Facebook. Every day, every day, the online casino sends you notifications…

The young adult online gamblers also noted a link between social casino games and online gambling, with several participants stating they transitioned to online gambling after playing for free on Facebook. One potential implication is the inflated payout rate offered by social casino games. The focus group members noted they win more frequently on social casino games, which provides them a sense of hope that they would be winning money had they been gambling for real. There was a general sense of needing to be “smart” and “savvy” to not fall prey to the tactics of online casinos and social media sites.

Once you play for fun, they sort of get people into the gambling, you think ok, this would be great if it were real money, so you try. That’s the way the websites make you go through that road.

They want you to win… if you are winning on Facebook and then you see [an advertisement] on the side to go online to play at party poker you will think if I can do this for free I can do this for real and then you go to do it for real and the next thing you know you are down $150 when you were getting Blackjack with the other one [social casino site].

There was a consensus that social casino games provided an excellent learning opportunity. Specifically, social casino games allow people to learn rules, procedures, and strategies to gamble.

So regardless of whether it is Facebook or just the practice sites on the online casinos, it’s a natural progression to start from social casino games: train, learn… then you realize you are not learning enough because people are not taking the game seriously, and then you move onto paying.

I don’t know those procedures so I don’t play (in casinos). But online who is going to yell at you online? So like you can just practice online and you can play lower [limit] tables. Basically you can practice online without other people yelling at you.

Participants also noted that after playing for free, they transitioned to online in part as most players who play for free do not play the game the ‘right way’

The difference between a table with real money and a table with fake money, the people with fake money, they don’t do the moves they usually do with their real money. You just mess around, you don’t really care “Oh I’m all in” – it’s like you don’t care. But at the real tables everyone plays the way they want to play. You get to learn a lot when you play.

I started playing online and when I played online without money I realized this was not really like anywhere close to the situation you would be in at a real table cause you don’t have any money on it, so I decided to start gambling with money.

However, not everyone perceived a link between social casino games and online gambling. These individuals explained that the interfaces of the games were so different (social media being much less sophisticated) that people who are attracted to one would likely not be attracted to the other.

I don’t think it’s as dangerous as people make it to be. If I want to switch from gambling on Facebook to a real site I just go to Google and type in poker and have it [online site].

You start playing poker with your friends and like you move from that step onto other things. I don’t think you go from Facebook to gambling. I don’t see that as a gateway at all.

In today’s technological world, young adults are exposed to a plethora of opportunities to engage in gambling activities, including simulated gambling games on social media sites. For some young adults, exposure to gambling and gambling-like activities may result in the over-involvement of gambling. In three focus groups, motivations that influenced young adults to engage in online gambling were explored. The participants noted several factors that motivated them to engage in online gambling: including suggestions from friends, the ease and accessibility of online gambling (compared to land-based venues), and incentives offered by the online gambling operators (e.g., $10 in free play).

The results of our present research may have important implications for the progression and maintenance of online gambling among young adults. First, several participants reported having been drawn to online gambling by bonuses offered by the gambling operators. Whilst incentives may help attract new customers, it should be noted that they may not be creating frequent customers. Indeed, free-play offers (e.g., bonus offers) bring customers into a gambling venue, but fail to generate significant increases in volume of play (Lucas et al. 2005 ). Having said that, given that online gambling is often framed as a risky form of gambling, in part due to the increased accessibility, whether operators should be allowed to offer incentives, especially amongst vulnerable population may be an important question which policy makers should address.

In addition to general factors that may motivate young adults to engage in online gambling, potential mechanisms for the social casino games-online gambling link were explored. One potential mechanism noted by the participants that may lead to the migration of online gambling from social casino games involves the use of advertisements by the online gambling operators. Specifically, it was noted that gambling operators sometimes use social casino games to advertise gambling activities without legal restrictions because it is a game. Indeed, as social casino games are not technically gambling activities, there is no regulation in regards to advertisement, prompting some to suggest that advertisements for social casino games be held to the same standard as gambling (Gainsbury et al. 2014 ). It has been suggested that these advertisements are more likely to appear to young adults and adolescents (Abarbanel et al. 2016 ). Further, advertisements for gambling (including social casino games) are frequent on social media sites and portray the positive aspects of gambling without any of the potential dangers (Gainsbury et al. 2016 ). Some of the participants in the focus groups reported moving from social casino games to real money gambling due to the constant advertisements of online casinos. As young adults may be more likely to be influenced by advertisements (Derevensky et al. 2010 ), some researchers have suggested that advertisements for social casino games be held to the same standard as gambling (Derevensky and Gainsbury 2016 ). Our results seem to provide support for this suggestion.

A second mechanism by which players migrated from social casino games to online gambling was via the inflated payout rates on social casino games. Note this mechanism was also identified in the two pathways model proposed by King and Delfabbro ( 2016 ). Specifically, participants felt an increased confidence in winning should they have engaged in real-money gambling. Further, several participants stated that their frequent wins on social casino games propelled them to try engaging in online gambling. This is in line with previous research, which found that a portion of casino gamers play these games to build up their ‘skill’ before migrating to gambling in land-based or online gambling venues (see Kim et al. 2016 ). However, the inflated payout rates may give players an inflated belief in the skill, and, of course, there is no skill if the game of choice is one of pure-chance, like a slot machine. In fact, social casino game outcomes are not based on random odds and mathematics, but are rather designed to enhance player enjoyment (Wohl et al. 2017 ). Because of this, the social casino gamer wins more than he loses (Sévigny et al. 2005 ), which in turn, may falsely increase their confidence in winning, as proposed by King and Delfabbro ( 2016 ). Providing further support that frequent wins and perception of skills as a process by which social casino games to lead to online gambling, Hollingshead et al. ( 2016 ) showed that playing social casino games for skill purposes have been linked to problematic gambling behaviors. In this light, it would behoove regulators to enforce payout rates that are similar to gambling activities, or at very least mandate social casino gaming operators to inform players of that social casino games are not based on random odds as their gambling counterparts.

According to Blaszczynski and Nower’s ( 2002 ) pathways model of problem and pathological gambling, there are three distinct subgroups of gamblers, each with different pathways that manifest in problem gambling behaviors. In the model, the starting point is ecological factors, which include increased availability and accessibility. In this way, social casino games may influence the development of problem gambling among young adults by providing ease of access and increased availability. Indeed, one of the concerns of social casino games is that although they purport to have age verifications, a UK study found that 300,000 youths aged 11–16 reported having engaged in free online gambling games in the past week (Parke et al. 2013 ). Furthermore, it is plausible that if social casino games lead to the development of problem gambling, it does through Pathway 1, the behaviourally conditioned gambler. This pathway includes cognitive mechanisms such as irrational beliefs and illusion of control, which may manifest due to the inflated payout rates on social casino games. That said, this is an assertion and would be in need of empirical support.

Limitations

Some limitations of the current study should be noted. First, we did not recruit a sufficient number of female online gamblers to ascertain different trends and cognitions that may be gender-specific. That said, studies have consistently found that online gamblers tend to be young males (Griffiths et al. 2009 ; for a review see Gainsbury 2015 ). Thus, we have confidence that the observed results maintain ecological validity. Secondly, the findings of the current project are not intended to be reflective of the college population as a whole. Rather, the findings are qualitative in nature and should be used to guide future research initiatives. Lastly, we recruited online gamblers to participant in the focus groups, rather than social casino gamers. Thus, the current study cannot speak to social casino games being a deterrent to online gambling (e.g., knowing you can’t win).

The Internet has drastically shaped the way in which people engage with the world, including with gambling activities. Furthermore, social networking sites have become a fabric of the modern day world. While the Internet and specifically social networking sites are a great medium to stay connected with loved ones, they have increasingly become an avenue to engage in gambling activities, including simulated forms of gambling (i.e., social casino games). The present research explored the motivations that push young adults to engage in online gambling, including the role of social casino games. Further research and attention is needed in this domain to mitigate the potential migration from gaming to gambling, specifically amongst those most vulnerable.

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Authors' contributions

MW, JD, and RG, conceptualized the research project. RG, conducted the focus groups. HK wrote the first draft of the manuscript and MW, JD, and RG, edited subsequent versions. All authors read and approved the final manuscript.

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Ethics approval was obtained from the corresponding author’s Research Ethics Board (REB) prior to data collection. All participants provided a written informed consent of their participation and to the use of their data for publication.

This research was funded by a grant from the Ontario Problem Gambling Research Centre (#3400).

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Kim, H.S., Wohl, M.J.A., Gupta, R. et al. Why do young adults gamble online? A qualitative study of motivations to transition from social casino games to online gambling. Asian J of Gambling Issues and Public Health 7 , 6 (2017). https://doi.org/10.1186/s40405-017-0025-4

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Received : 25 April 2017

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DOI : https://doi.org/10.1186/s40405-017-0025-4

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• Online gambling
• Adolescent gambling
• Social media
• Social casino games
• Focus groups
• Qualitative research