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Alex M. T. Russell Experimental Gambling Research Laboratory, CQUniversity, Australia

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Nerilee Hing Experimental Gambling Research Laboratory, CQUniversity, Australia

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Philip Newall Experimental Gambling Research Laboratory, CQUniversity, Australia
University of Bristol, United Kingdom

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Nancy Greer Experimental Gambling Research Laboratory, CQUniversity, Australia

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Cassandra K. Dittman Experimental Gambling Research Laboratory, CQUniversity, Australia

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Hannah Thorne Experimental Gambling Research Laboratory, CQUniversity, Australia

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Matthew Rockloff Experimental Gambling Research Laboratory, CQUniversity, Australia

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Open access

Abstract

Background and aims

Simulated gambling products, like loot boxes and social casino games, contain gambling elements, but are not classified as gambling. They are available to minors, raising concerns about a “gateway effect” into gambling. This study examined the time course of young people's engagement in simulated and monetary gambling, and associations between simulated gambling and gambling problems and harm. A necessary, although not sufficient, condition for simulated games leading to real money gambling is that simulated play must come first.

Method

Participants were 1,026 young adults (aged 18–25 years) who played video games in the last year. They reported the age at which they first took part in seven simulated and twelve monetary gambling products, and current gambling problems and harm.

Results

First use of loot boxes and video games with gambling content tended to precede monetary gambling. Forms where gambling is a core gameplay element, such as social casino and demonstration games, tended to follow some monetary gambling forms. Engagement in most simulated gambling products was associated with greater harm from monetary gambling.

Discussion

The findings leave open the possibility of a catalyst pathway from youth engagement in loot boxes and games with gambling content to later monetary gambling, but causal psychosocial mechanisms remain unclear. However, a pathway from social casino and demonstration games to monetary gambling appears less likely, which may instead reflect containment or substitution effects. Simulated gambling disproportionately attracts youth who are vulnerable to gambling problems and harm, indicating the need for consumer protection measures.

Abstract

Background and aims

Simulated gambling products, like loot boxes and social casino games, contain gambling elements, but are not classified as gambling. They are available to minors, raising concerns about a “gateway effect” into gambling. This study examined the time course of young people's engagement in simulated and monetary gambling, and associations between simulated gambling and gambling problems and harm. A necessary, although not sufficient, condition for simulated games leading to real money gambling is that simulated play must come first.

Method

Participants were 1,026 young adults (aged 18–25 years) who played video games in the last year. They reported the age at which they first took part in seven simulated and twelve monetary gambling products, and current gambling problems and harm.

Results

First use of loot boxes and video games with gambling content tended to precede monetary gambling. Forms where gambling is a core gameplay element, such as social casino and demonstration games, tended to follow some monetary gambling forms. Engagement in most simulated gambling products was associated with greater harm from monetary gambling.

Discussion

The findings leave open the possibility of a catalyst pathway from youth engagement in loot boxes and games with gambling content to later monetary gambling, but causal psychosocial mechanisms remain unclear. However, a pathway from social casino and demonstration games to monetary gambling appears less likely, which may instead reflect containment or substitution effects. Simulated gambling disproportionately attracts youth who are vulnerable to gambling problems and harm, indicating the need for consumer protection measures.

Introduction

Simulated gambling games are software products that incorporate elements of gambling, but without monetary payouts. Researchers have raised concerns that simulated gambling products may normalise gambling among young people, and serve as a “gateway drug” to the subsequent use of monetary gambling, particularly because they are available to people under the legal gambling age and many appeal to adolescents (Armstrong, Rockloff, Browne, & Li, 2018; Hing, Browne, Rockloff, Lole, & Russell, 2022, Hing, Dittman et al., 2022, Hing, Rockloff et al., 2022; Kim, Wohl, Gupta, & Derevensky, 2017; Kristiansen, Camilla, Reventlov, & Malling, 2018; Kristiansen, Trabjerg, Lauth, & Malling, 2018). However, relatively little research has examined this gateway effect or even the time course of simulated and monetary gambling engagement amongst young people.

What is simulated gambling?

Gambling has been defined as risking something of value, to win something of value, with the outcome determined at least in part by chance (King, 2018). Simulated gambling refers to games that imitate many core characteristics of gambling (e.g., the look, sound and actions) but do not provide an opportunity for a cash payout (Hing, Russell, Browne, et al., 2021; Hing, Russell, King, et al., 2021). They therefore do not incorporate all definitional components of gambling. Several forms of simulated gambling exist. The main products are described below, including how they differ from monetary gambling products, as also highlighted in a taxonomy by King (2018).

Social casino games are apps or programs that replicate gambling products, such as slots, card or table games. Many are free to play, but players can pay for additional credits or to unlock levels within the game (Gainsbury, Hing, Delfabbro, & King, 2014; Gainsbury, Russell, & Hing, 2014; Gainsbury, Hing, Delfabbro, Dewar, & King, 2015; Gainsbury, King, et al., 2015; Gainsbury, Russell, Wood, Hing, & Blaszczynski, 2015; Hing, Lole et al., 2023; Hing, Rockloff, & Browne, 2023; Hing, Russell et al., 2023). Demo games have a similar concept, where players try a practice version of a real money gambling product, often on real online casino websites. In both social casino games and demo games, the core gameplay element is a gambling game; however, any credits won cannot be withdrawn for real money (Hing, Lole et al., 2023; Hing, Rockloff, & Browne, 2023; Hing, Russell et al., 2023; King, 2018; King & Delfabbro, 2020).

Some video games incorporate gambling elements, such as spinning wheels and slot games, as part of a broader gameplay experience, rather than gambling being the core element (King et al., 2018). For example, in Grand Theft Auto, players complete quests and explore a large online environment, but gambling is not involved in most of these quests. However, the game includes a virtual casino, where players can win virtual credits that are only valuable within the game (Hing, Russell, Browne, et al., 2021; Hing, Russell, King, et al., 2021; Hing, Lole et al., 2023; Hing, Rockloff, & Browne, 2023; Hing, Russell et al., 2023).

Loot boxes are digital containers that can be purchased or won within most popular video games, such as sports games, first-person shooter and strategy games (Rockloff, Browne, Greer, Armstrong, & Thorne, 2020; Rockloff, Russell, et al., 2020; Zendle, Cairns, Barnett, & McCall, 2020) and where gambling is not a core gameplay element. However, loot boxes involve gambling mechanics and some may meet the technical definition of gambling (Drummond, Sauer, Hall, Zendle, & Loudon, 2020; Drummond & Sauer, 2018; Liu, 2019). Some loot boxes can be earned through extensive gameplay (“grinding”), but players may also purchase loot boxes with real money. Players have a chance of winning rare items, which may be sold in online marketplaces or used as currency on skin gambling websites (Hing, Rockloff et al., 2022). Some loot boxes therefore meet definitions of gambling, as players can pay to play, can win something of real-world value, and the prize is based on chance. Nevertheless, loot boxes are not classified as gambling in most jurisdictions and are, therefore, available to people under the legal gambling age.

Real money can also be won in some free-to-enter fantasy sports competitions (Marchica, Zhao, Derevensky, & Ivoska, 2017; Tacon & Vainker, 2017). In fantasy sports, participants create and manage virtual teams of players, based on real players in real sporting competitions. Points are allocated based on how the relevant players perform in the real-world competition (King, 2018). These competitions are not classified as gambling because players do not stake something of value, although paid-entry competitions exist. However, prize money can often be won, and there is a degree of chance in how each player performs in real games each week.

Engagement in simulated gambling, and links to gambling behaviour and gambling problems

Gambling has become increasingly normalised among adolescents, partly through its frequent advertising (Hing, Lole et al., 2023; Hing, Rockloff et al., 2023; Hing, Russell et al., 2023; Pitt, Thomas, & Bestman, 2016), but also through gambling content in spaces that young people frequent, such as social media platforms (Gainsbury, King, et al., 2015) and video games (King, 2018; Rockloff, Russell, et al., 2020; Zendle et al., 2020). Simulated gambling is common amongst youth in many countries (Hayer, Rosenkranz, Meyer, & Brosowski, 2019; Hing, Dittman et al., 2022; Gambling Commission, 2019). For example, a recent study in Australia found that 36.5% of adolescents aged 12–17 years reported purchasing loot boxes in the past 12 months, 31.7% reported playing video games with gambling content, 14.2% played social casino game apps, 14.2% played demo games and 11.8% played gambling-style games on social media sites (Hing, Russell, King, et al., 2021). Another Australian study found evidence for a possible generational shift in interest amongst young people towards simulated gambling, including those under the legal gambling age (Russell et al., 2020).

Young people are overrepresented among people experiencing problems from monetary gambling (Australian Institute of Family Studies, 2016), and youth is a consistent risk factor for gambling-related harm (Gainsbury, Russell, et al., 2015; Hing, Russell, Tolchard, Nower, 2016; Hing, Russell, Vitartas, & Lamont, 2016; Johansson, Grant, Kim, Odlaug, & Götestam, 2009; Russell, Hing, Li, & Vitartas, 2019).

Several studies have found that engagement in simulated gambling is associated with monetary gambling (Dussault et al., 2017; Hayer, Kalke, Meyer, & Brosowski, 2018; Rockloff, Browne, et al., 2020), including amongst adolescents (Elton-Marshall, Leatherdale, & Turner, 2016; King, Delfabbro, Kaptsis, & Zwaans, 2014; Veselka, Wijesingha, Leatherdale, Turner, & Elton-Marshall, 2018). For example, in two Australian samples, past-month engagement in loot boxes, social casino games, demo games, and games with gambling content were each associated with past-month participation in most forms of monetary gambling (Hing, Dittman et al., 2022; Hing, Rockloff et al., 2022). Further, research consistently links engagement in simulated gambling to a heightened risk of monetary gambling problems in youth (Gainsbury, Hing et al., 2015; King, Delfabbro, Katsis et al., 2014; Zendle, Meyer & Over, 2019), even when controlling for monetary gambling participation (Hing, Dittman, et al., 2022). However, these correlational studies do not provide evidence of migration over time from simulated gambling to monetary gambling.

Evidence of migration from simulated gambling to monetary gambling

Limited research has examined migration from simulated to monetary gambling among young people. Spicer et al. (2022) found that 19.9% of 1,102 individuals who had purchased both loot boxes and taken part in gambling self-reported a “gateway effect” from loot boxes to monetary gambling. Three longitudinal studies provide stronger evidence about the time course of simulated and monetary gambling amongst young people. In an Australian longitudinal study, young people who played simulated gambling games during adolescence (aged 16–17 years) were more likely to spend money on gambling in early adulthood (aged 18–19 years), including on race betting, sports betting and casino gambling (Sakata & Jenkinson, 2022). A Canadian study with 1,220 young people who had never gambled at Wave 1 (Dussault et al., 2017) found a migration over 3 waves from simulated poker to real money poker, but not to other gambling forms. Hayer et al. (2018) conducted a longitudinal study on 1,178 German school students and focussed on social casino games, games with gambling components, and demo games. Migration occurred over time from simulated to monetary gambling, but only for social casino games. Additional analysis revealed that simulated gambling impacted gambling problems mainly through the indirect effects of gambling frequency and erroneous cognitions (Brosowski, Turowski, & Hayer, 2020).

Potential causal mechanisms for migrating from simulated to monetary gambling

While there is evidence that some young people transition from simulated gambling to monetary gambling, it is unclear whether a causal mechanism exists. Nonetheless, researchers have identified several features of simulated gambling that might foster psychosocial processes and behaviours that lead to real-money gambling. These include play rewarded by in-game acquisition, even of non-financial rewards, inflated and more opaque odds of winning, randomised rewards, monetisation, and features that may train young people how to gamble, normalise it, and encourage erroneous cognitions, persistence and dependency (Armstrong et al., 2018; Hing, Browne et al., 2022; King & Delfabbro, 2016, 2020). In addition, Kim, Wohl, Salmon, Gupta, and Derevensky (2015) identified a plausible migration motivation whereby young people transition to monetary gambling when they tire of being able to win only virtual rewards in social casino games, demo games and loot boxes. Based on a literature review, King and Delfabbro (2016) modelled a “catalyst pathway” that integrates risk factors related to simulated gambling that can catalyse monetary gambling and gambling problems. This model includes risk factors across several domains: social (e.g., entry into a gambling subculture), behavioural (e.g., opportunities for early big wins, large bets, monetary expenditure and persistence), cognitive (e.g., can foster erroneous beliefs about gambling, such as the role of skill and strategy) and emotional (e.g., arousal, relief from negative mood states, desensitising to losses). However, as outlined in Table 1, simulated gambling activities have different structural characteristics and differ from monetary gambling in different ways. For example, in free fantasy sports with prizes, something of value cannot be staked, but something of value can be won. In contrast, in paid social casino games, money can be lost but not won. Therefore, migration from simulated gambling to monetary gambling may involve varying psychosocial and behavioural processes, depending on the activity.

Table 1.

How different simulated gambling products differ from monetary gambling products

Note: The columns represent three typical components of monetary gambling products. “Some” means that at least some specific products in this category of simulated gambling may meet this definition. For example, some free loot boxes may contain rewards that can have real-world value.

The present study examined temporal evidence of plausible migration pathways from seven simulated gambling forms to each of 12 monetary gambling forms. A minimum, although not sufficient, condition for simulated gambling being a dominant factor in motivating a migration to monetary gambling is that it must come first in time. Consequently, this study examines which games are typically (on average) played first. Because the causal direction is not clear for different products, no hypotheses were developed and the study is exploratory. A secondary aim of the study was to determine which simulated gambling products were associated with gambling, gambling problems and gambling harm.

Method

Participants, inclusion criteria, exclusions and completion rate

Participants were recruited via Qualtrics, an online market research panel aggregator, in November 2020. Survey participants were residents of Australia (50% from Victoria, the location of the funding body), aged 18–25 years, and had played video games (not necessarily simulated gambling games) within the last 12 months. This sample therefore included people who took part in either simulated or monetary gambling forms, neither, or both. The inclusion of non-gamblers was crucial, as exploring transitions to gambling requires comparisons with people who have not transitioned. The video game inclusion criterion was a practical one, to ensure that the sample did not include a large proportion of people who take part in neither simulated nor monetary gambling. Quotas were set to ensure approximately equal numbers of males and females. The youthful age range was chosen due to the recency of products, and thus older adults would most likely have not been able to engage with many simulated gambling products first.

A total of 2,619 participants started the survey, but 1,244 were excluded during the survey for being outside the age range (794), not providing consent (167), failing an attention check (110), not being video gamers (110), not providing their location (12), and being from Victoria after the quota was filled (51). After data collection, Qualtrics and the research team examined the data for poor quality responses, excluding a further 120 participants for one or more of the following reasons: duplicate responses (76), poor quality responses (36), straightlining (selecting the same answer, e.g., “agree”, throughout multiple scales in the survey; 19), speeding (completing the survey in under one-third the median completion time from an initial soft launch; 7) and having an IP address outside of Australia (5). Of the remaining 1,255 eligible participants, 1,026 completed the survey, for a completion rate of 81.8%.

Procedure

Participants were recruited via Qualtrics, who recruited participants via online panel providers. Participants were reimbursed with a non cash-incentive in line with the usual practices of their online panel provider. A contact rate cannot be calculated as it is not known how many participants were contacted. Participants were shown an information page that outlined the purpose of the study, and conveyed that the survey was anonymous, voluntary and that they could withdraw at any time prior to survey submission. Participants were given the contact details of the lead investigator should they have questions or concerns, although no one contacted us. Survey participants were asked to provide consent before taking part. Median completion time was 18.3 min.

Measures

The measures below are shown in the order they appeared in the survey.

Screening and quota questions

Participants reported their age (in years), gender (male, female, other), postcode and how often they played video games, including games on their smartphone, tablet, PC or console (seven-point Likert scale from 1 = never in the last 12 months, 2 = less than once a month, 3 = about once a month, 4 = 2–3 times a month, 5 = about once a week, 6 = 2–3 times a week and 7 = 4 or more times a week. This response scale is commonly used in Australian studies (e.g., Hing, Russell, Browne, et al., 2021; Russell et al., 2020).

Engagement with simulated and monetary gambling forms

Participants were asked whether they had taken part in each of the seven simulated gambling forms and 12 monetary gambling forms at any point in their life (see Table 2). The classification of simulated and monetary gambling is based on recent Australian studies, including amongst adolescents (Hing, Russell, King, et al., 2021) and young adults (Russell et al., 2020). The seven simulated forms were:

  1. -Loot boxes (free) (opening a loot box that the person earned during the game, but did not pay for),
  2. -Social casino games (free) (gambling-like games, like simulated EGMs, poker, roulette, on an app or social network, played for free),
  3. -Playing video games that include gambling content (such as Grant Theft Auto's casino level),
  4. -Loot boxes (paid) (buying a loot box with real money, or with virtual currency that was purchased with real money),
  5. -Social casino games (paid) (gambling-like games, like simulated EGMs, poker, roulette, on an app or social network, where they person has paid to play),
  6. -Fantasy sports (free) (fantasy sports or daily fantasy sports competitions that do not require an entry fee), and
  7. -Demon games (free demonstration or practice games on real gambling websites or apps, generally designed to help players learn to play).

Table 2.

Sample demographics, PGSI and NODS-CLiP (N = 1,026)

VariableLevelN%
GenderMale41240.2
Female60659.1
Other80.8
State of residenceVictoria51249.9
Elsewhere in Australia51450.1
Marital statusSingle/never married66464.7
Living with partner/de facto25424.8
Married949.2
Divorced or separated121.2
Widowed20.2
EducationDid not complete year 12 or equivalent636.1
Completed year 12 or equivalent40039.0
Completed trade or technical certificate or diploma19218.7
Completed an undergraduate qualification28828.1
Completed a postgraduate qualification838.1
Country of birthAustralia81279.1
Other21420.9
Aboriginal or Torres Strait Islander statusNeither Aboriginal nor Torres Strait Islander95793.3
Yes, Aboriginal494.8
Yes, Torres Strait Islander151.5
Yes, both Aboriginal and Torres Strait Islander50.5
Gambling problemsNon-gambler (last 12 months)62460.8
last 12 monthsNon-problem10710.4
(PGSI)Low-risk545.3
Moderate-risk727.0
Problem16916.5
Lifetime gamblingNon-gambler (lifetime)14313.9
problemsNo lifetime problems43242.1
(NODS-CLiP)Problems during lifetime45144.0

Note: PGSI = Problem Gambling Severity Index. GHS-10 = Gambling Harms Scale, 10 items. NODS-CLiP = National Opinion Research Centre Diagnostic and Statistical Manual of Mental Disorders version IV Screen for Gambling Problems, Control, Lying and Preoccupation.

The twelve monetary gambling forms were: scratch cards, lottery tickets, sports betting, race betting, betting on esports, betting on novelty events (e.g., elections), EGMs, bingo, casino games, Keno, skin gambling and betting on fantasy sports.

For every form that a participant endorsed, they were asked how frequently they had engaged in that form in the last 12 months, using the same seven-point Likert scale for video games. They were also asked the age at which they had first taken part and the age at which they had most recently taken part in each endorsed activity. Survey programming ensured that participants could not enter ages that were logically inconsistent, such as first taking part in an activity at an age older than their current age. Participants were also reminded that the survey was anonymous to reduce concerns that participants might not report taking part in monetary gambling activities under the age of 18, which is the legal gambling age in Australia.

Gambling problems

Gambling problems over the last 12 months were measured using the Problem Gambling Severity Index (PGSI; Ferris & Wynne, 2001). The PGSI consists of nine items, with response options from never (0) to almost always (3). Responses are summed for a total out of a possible 27 points. The PGSI was categorised based on the cut-offs developed by Ferris and Wynne: non-problem gambler (PGSI = 0), low-risk gambler (PGSI = 1–2), moderate-risk gambler (PGSI = 3–7), and “problem gambler” (PGSI = 8–27). Cronbach's alpha in the current sample was 0.94.

Gambling problems over the lifetime were assessed using the NODS-CLiP (Toce-Gerstein, Gerstein, & Volberg, 2009). The NODS-CLiP was asked of all participants who reported engaging with any monetary gambling activity during their lifetime, including outside of the last 12 months. This scale consists of three items, each with a no/yes response. Endorsement of any item indicates gambling problems in the lifetime. KR-20 in the current sample was 0.60. It is important to note that the NODS-CLiP consists of only three items, and short scales typically have lower internal consistency. This does not necessarily indicate poor reliability (Ziegler, Kemper, & Kruyen, 2014). Using the Spearman-Brown prophecy formula, if the scale had nine items like the PGSI, reliability would be 0.82.

Gambling harm

The Gambling Harms Scale 10-item version (GHS-10, previously known as the Short Gambling Harms Screen or SGHS; Browne, Goodwin, & Rockloff, 2017) was used to measure gambling harm. The GHS-10 consists of 10 items, each with a no (0) or yes (1) response option. Endorsed items are summed for a total between 0 and 10. Cronbach's alpha in the current sample was 0.86.

Demographics

In addition to age, gender and postcode, participants were asked to report their marital status, highest level of education, country of birth, and Aboriginal and/or Torres Strait Islander status (Table 1).

Data analysis

Linear and logistic regressions were employed to determine associations between lifetime use of each simulated gambling form and monetary gambling outcomes (behaviour, problems, harm). The ‘first age’ response for each form was used to determine the mean difference between age of onset for each possible pairing of simulated and monetary forms, noting that sequencing was done separately for each pair. The mean difference variables were trimmed to range between −6 and 6. Wilcoxon signed-rank tests were conducted to determine whether the simulated or monetary form was significantly more likely to occur at a younger age for each possible pairing. There were no missing data because all survey questions were compulsory, unless questions were skipped by design (e.g., if participants did not bet on sports in their lifetime, they did not have answers for subsequent sports betting variables). Raw PGSI scores were positively skewed and therefore log-transformed (+1) for analysis. Data were analysed using a combination of SPSS v28 and R v4.3.0. An alpha of 0.05 was used throughout, but results are reported for p < 0.01 and p < 0.001 to allow for corrections for multiple comparisons.

Ethics

The study was assessed and approved by the CQUniversity Human Research Ethics Committee, approval 22,525. All participants were shown an initial description of the study, which outlined the nature of the questions obtained within the subsequent survey. This description also outlined that participation was voluntary and that they could withdraw at any time. Participants then indicated their consent before continuing with the survey. Participants who did not consent were thanked for their time before they exited the survey. Participants who did not complete the survey were deemed to have withdrawn and excluded from the final analyses.

Results

Demographics

Full sample demographics are reported in Table 2. The sample included slightly more females than males (59.1% vs. 40.2%) and eight participants identified as a gender other than male or female. Participants were aged from 18 to 25 years, with a mean of 21.87 (SD = 2.32). Approximately half of the participants reported living in the state of Victoria and half elsewhere in Australia. In line with the sample participants' young ages, almost two-thirds were single/never married, and the most common educational qualifications were completing year 12 (high school), a trade or technical certificate or diploma, or an undergraduate degree. Close to 80% of participants were born in Australia, and 6.7% identified as Aboriginal and/or Torres Strait Islander. PGSI classifications showed that, amongst those who had gambled in the last 12 months, approximately three-quarters had experienced some degree of problems during this period (low-risk, moderate-risk and problem). Amongst those who gambled in their lifetime, approximately half had experienced problems during their lifetime (NODS-CLiP).

Engagement with simulated and monetary gambling forms

Engagement with simulated and monetary gambling forms is shown in Table 3. Importantly, because this is not a probability sample, these figures should not be interpreted as prevalence figures, but are reported for context. Around half of the participants had taken part in opening free loot boxes, playing video games with gambling content, and playing free social casino games in their lifetime. Around a quarter to a third had taken part in free fantasy sports, paid loot boxes, paid social casino games and demo games. The most popular monetary forms (lifetime) were scratch cards, lottery tickets, sports betting, EGMs, bingo, race betting and casino games. Newer forms, such as esports betting, fantasy sports betting and skin gambling were less popular. A similar pattern was observed with engagement in the last 12 months.

Table 3.

Lifetime engagement, last 12 months engagement, mean first age of engagement and percentage first engaging in each simulated and monetary gambling form prior to age 18 (N = 1,026)

CategoryFormsLifetimeLast 12 monthsFirst ageBefore age 18
n%n%MeanSD%
SimulatedLoot boxes (free) (opening a loot box that the person earned during the game, but did not pay for)57956.448647.417.23.947.5
Social casino games (free) (gambling-like games, like simulated EGMs, poker, roulette, on an app or social network, played for free)51249.937936.918.53.629.7
Playing video games that include gambling content (such as Grant Theft Auto's casino level)48347.138437.417.14.148.9
Loot boxes (paid) paid loot boxes (buying a loot box with real money, or with virtual currency that was purchased with real money)33532.727026.318.73.730.1
Demo games (free demonstration or practice games on real gambling websites or apps, generally designed to help players learn to play)33532.726325.619.43.416.7
Fantasy sports (free) (fantasy sports or daily fantasy sports competitions that do not require an entry fee)26525.820119.618.44.131.3
Social casino games (paid) (gambling-like games, like simulated EGMs, poker, roulette, on an app or social network, where they person has paid to play)22622.018117.619.83.38.8
MonetaryScratch cards55754.339438.417.93.221.7
Lottery tickets49748.438937.918.62.411.3
Sports betting46445.238037.019.23.011.6
EGMs42641.531630.818.92.26.1
Bingo41640.525324.718.03.828.1
Race betting39538.530930.119.13.114.2
Casino games33532.723122.519.42.45.4
Novelty betting23322.718918.420.12.97.7
Keno22421.816015.619.03.110.7
Esports betting19719.216416.019.73.513.2
Skin gambling14113.711711.419.33.317.0
Fantasy sports betting12912.61019.820.22.97.0

Note: Mean first age is based on people who took part in each form at any point during their lifetime. The before age 18 column shows the percentage of participants who had engaged in each form who reported engaging in each form in their lifetime prior to the age of 18. EGMs = electronic gaming machines.

Associations between exposure and simulated forms, and subsequent traditional gambling behaviour and harm

Table 4 shows the relationship between each of the seven simulated gambling forms and monetary gambling outcomes. Engagement with any simulated gambling form during one's lifetime, apart from free loot boxes, was associated with monetary gambling during the lifetime, and all forms were associated with monetary gambling in the last 12 months. Most simulated forms, apart from free loot boxes, were associated with a higher (log+1) PGSI score (i.e., gambling problems in the last 12 months), and all forms were associated with being classified as experiencing gambling problems during their lifetime (NODS-CLiP). All forms, other than free loot boxes and playing video games with gambling content, were also associated with higher gambling harm scores (last 12 months). When controlling for multiple comparisons, free loot boxes were not associated with any gambling behaviour, problems or harm outcomes.

Table 4.

Associations between engagement with simulated gambling forms (lifetime) and engagement in monetary gambling, gambling problems and gambling harm

Note: Coefficients for logistic regressions are odds ratios (null value = 1), and for linear regressions are standardised coefficients (null value = 0). Values in brackets are 95% confidence intervals. *p < 0.05, **p < 0.01, ***p < 0.001. Ref = reference. PGSI = Problem Gambling Severity Index. GHS-10 = Gambling Harms Scale, 10 items. NODS-CLiP = National Opinion Research Centre Diagnostic and Statistical Manual of Mental Disorders version IV Screen for Gambling Problems, Control, Lying and Preoccupation.

Temporal sequence of taking part in simulated and monetary forms

Table 5 shows the temporal sequence of the age at which people reported first taking part in each possible pairing of simulated and monetary gambling forms. Each cell is based on people who took part in both forms. For example, the cell depicting the relationship between free loot boxes and scratch cards is based only on the people who took part in both free loot boxes and scratch cards at some point in their life. The figures in each cell are the mean difference of the age at which participants first took part in each form. For free loot boxes and scratch cards, this figure is −0.38, indicating that the age at which people first took part in loot boxes was 0.38 years (on average) before they took part in scratch cards. Red cells indicate that the simulated form was significantly more likely to occur at a younger age, while green cells indicate that the simulated form was significantly more likely to occur at an older age, compared to the monetary gambling form.

Table 5.

Difference between mean age of first taking part in each simulated form, and each monetary gambling form

Note: Some cells have low cell counts, especially in the lower right of the table, and are therefore underpowered. Negative numbers (red cells) indicate that the age of first use of the simulated form was statistically significantly lower than that for the monetary form. Positive numbers (green cells) indicate that the monetary gambling form was statistically significantly more likely to come first. Tests are Wilcoxon signed-rank tests. Due to outliers, reported mean differences are trimmed at −6 and 6 years difference. *p < 0.05, **p < 0.01, ***p < 0.001. EGMs = electronic gaming machines.

As can be seen in Table 5, free loot boxes and playing video games with gambling content were significantly more likely to occur before any monetary gambling activity, except for bingo in the case of video games with gambling content. Free fantasy sports were significantly more likely to occur before casino games, novelty betting and paid fantasy sports betting. Paid loot boxes were significantly more likely to occur before sports betting, casino games, novelty betting, esports betting, skin gambling and fantasy sports betting, but after scratch cards and lottery tickets. Free social casino games were significantly more likely to occur before novelty betting, but after scratch cards, lottery tickets and bingo. Paid social casino games and demo games were significantly more likely to occur after scratch cards, lottery tickets, sports betting, EGMs and bingo. In the case of paid social casino games, they were also more likely to occur after race betting and casino games. When controlling for multiple comparisons, the general pattern of results is similar, but some results were no longer statistically significant. Free social casino games were no longer more likely to occur after lottery tickets, paid social casino games were no longer more likely to occur after casino games, and demo games no longer more likely to occur after sports betting.

Discussion

This study aimed to explore patterns of first use for simulated and monetary gambling, and links between simulated gambling and gambling problems and harm. Lifetime engagement in all simulated gambling forms (except free loot boxes) was associated with monetary gambling engagement during the lifetime and last 12 months, and gambling harms and problems. These findings add to the growing and consistent evidence that participating in simulated gambling statistically predicts monetary gambling, and more concerningly, harmful gambling, among young people (Baggio et al., 2016; Hing, Dittman, et al., 2022; Hing, Lole, et al., 2023; Hing, Rockloff, et al., 2023; Hing, Russell, et al., 2023; Hing, Rockloff, et al., 2022; King et al., 2014; Rockloff et al., 2021; Wardle, 2019). However, these correlations cannot determine whether these associations are due to psychosocial and behavioural factors at play in a possible catalyst effect (King & Delfabbro, 2016) or to third-party variables (Hing, Dittman et al., 2022).

Importantly, therefore, examining the age of uptake of simulated and monetary gambling is a preliminary step towards untangling these pathways. This study found that young people commonly first engaged in free loot boxes and games with gambling content between the ages of 13 and 17. This likely reflects the incidental access that adolescents have to these activities because they are embedded and unavoidable within the digital games they frequently play (Hing, Lole, et al., 2023). However, engagement with simulated forms that they need to deliberately seek out and where gambling is the key element of gameplay (e.g., paid social casino games and demo games) was less common before the age of 18. Unsurprisingly, there was a large rise in the uptake of monetary gambling forms around the legal gambling age of 18, when young people are likely to have more disposable income and gambling is often seen as a “rite of passage” (Kristiansen, Trabjerg, & Reith, 2015; McCarthy, Thomas, Pitt, Daube, & Cassidy, 2020; Reith & Dobbie, 2011).

Untangling pathways between simulated and monetary gambling is further informed by information about their temporal sequencing. Free loot boxes and playing video games with gambling content were significantly more likely to precede every monetary gambling form (except bingo for video games with gambling content). Similarly, free fantasy sports and paid loot boxes preceded several monetary forms. These findings at least partly reflect that free fantasy sports, loot boxes and games with gambling content are more accessible to young people under the legal gambling age, compared to monetary gambling products. Nonetheless, this temporal sequence also leaves open the possibility of a migration pathway, where these simulated gambling games catalyse monetary gambling uptake through increasing social, behavioural, cognitive and emotional risk factors (King & Delfabbro, 2016). For example, engagement in these simulated activities can increase social influences on young people to gamble, through exposure to gambling subcultures and the social cache gained among peers from wins (Hing, Browne et al., 2022). These simulated games can also encourage behaviours such as persistence and real money expenditure to acquire prizes (Armstrong et al., 2018; Hing, Lole, et al., 2023). Their cognitive effects can lead to a misperception of the role of chance in gambling and enhanced confidence in gambling “skill” (King & Delfabbro, 2016). In the emotional domain, these activities can foster arousal, reduced sensitivity to in-game losses, relief from negative mood states, and impulses to gamble with real money, especially among young people who are vulnerable to gambling problems (Armstrong et al., 2018; King & Delfabbro, 2016).

These results may also reflect that these particular simulated and monetary gambling products appeal to the same consumers. For example, gambling on lottery tickets typically preceded paying for loot boxes. These two activities are functionally similar and may therefore have a similar appeal to young people. Nonetheless, lottery products are often gifted to children from a young age (Hing, Russell, King, et al., 2021; Kristiansen et al., 2015), so this temporal sequence also reflects ease of access.

The forms of simulated gambling where gambling is the main gameplay element (social casino games and demo games) tended to first occur after engagement in many gambling forms, including some of the more harmful forms like EGMs and sports betting (Browne et al., 2023). These results raise doubts about the extent of migration for these products. Instead, this temporal sequence may reflect a “containment effect” where engaging in simulated gambling in a supportive and educative environment might be used to build resilience against excessive gambling (King & Delfabbro, 2016). Simulated gambling might also be used as a substitute for monetary gambling in order to curtail harmful gambling (Hing, Dittman et al., 2022), although few people appear to use this strategy (Gainsbury, Hing et al., 2015, Kristiansen, Camilla et al. 2018; Kristiansen, Trabjerg, et al., 2018; Rockloff, Browne et al., 2020; Rockloff, Russell et al., 2020). It is more likely that social casino and demo games appeal to people who already engage in monetary gambling due to their similar structural characteristics, which therefore enable their use as practice games, and because both activities appeal to particular types of young people (Armstrong et al., 2018; Hing, Dittman et al., 2022). It is well recognised that certain psychological characteristics, including poor social connectedness, higher impulsivity, emotional and attentional problems, social dysfunction, and maladaptive coping strategies, increase young people's vulnerability to gambling engagement and gambling problems (Riley, Oster, Rahamathulla, & Lawn, 2021).

Irrespective of the typical sequence of what games, simulated or monetary, are played first, simulated gambling games appeal to young people who are vulnerable to gambling harm. Policymakers should therefore consider improved consumer protection and harm minimisation measures for simulated gambling games, such as limit setting, age-gating, provision of help line details and other measures that providers of monetary gambling products must provide.

Limitations and suggestions for future research

The study relied on self-report, and there may be some recall bias, including in the age at which participants first took part in each activity. However, any biases were likely to be similar across forms for any individual, and key analyses were based on the sequence of activities per participant. Some participants may have been reluctant to report illegally taking part in some monetary forms when underage. We attempted to minimise this bias by reminding participants at this point that the survey was anonymous. Some forms are more prevalent than others, and therefore more people may take part in more prevalent forms earlier. For instance, a primary reason why people use free loot boxes before gambling on monetary forms is because loot boxes are present in the most popular games that young people play and they are therefore are more likely to encounter these products first. The study examined only the age of first participation in each simulated and monetary gambling activity and not the degree of engagement, such as frequency and time and money spent. Research that examines relationships between the level of engagement in simulated and monetary gambling activities, over time, is needed to clarify temporal and migration effects. Lastly, the research made use of a paid online research panel, so the sample may not be representative of the population of 18–25 year olds in Australia. However, paid online samples tend to support similar relationships between variables to those found in representative samples, and the current sample allows for better exploration of relatively rare activities (Russell, Browne, Hing, Rockloff, & Newall, 2022). As noted previously, our methodology does not allow for causal inference, but only suggests which games, simulated or monetary, are played first. It is a necessary (although not sufficient) condition for causation, however, that simulated gambling should be played before monetary gambling. The present results can provide evidence for future exploration of major causal pathways that might influence migration from simulated gambling to monetary gambling. Importantly, research is needed into pathways from simulated gambling to gambling addiction. Studying potential pathways, such as those seen in online addictive behaviours (Brand, 2022), can help to understand this pathway and how it might be disrupted. For example, the Pathways Model of problem and pathological gambling (Blaszczynski & Nower, 2002) has been applied to smartphone use (Canale et al., 2021) and might contribute to explaining migration from simulated gambling to gambling addiction. Broader public health models would also be informative to understand how contextual factors, such as products and environments, impact on this pathway.

Conclusion

While this study commenced with the ostensibly simple purpose of examining patterns of uptake of simulated and monetary gambling activities, it has instead revealed the complexity of these patterns and their possible explanations. In addition to the possible catalyst, containment and substitution effects of simulated gambling on monetary gambling, the relative access that young people have to these activities while growing up, may play important roles in how they engage with these activities. In alignment with a public health perspective on gambling (Hilbrecht et al., 2020), other individual, social and contextual factors are also likely to influence the interplay between simulated and monetary gambling. Additional complexity is apparent because simulated gambling activities vary widely in their characteristics and their resemblance to monetary gambling products. These factors suggest that future research should be open to identifying several pathways amongst young people on the road from simulated gambling to monetary gambling, or vice versa.

Funding sources

This work was funded as part of Victorian Responsible Gambling Foundation Grants, Round 10 – Early Career Researcher (GR10/19/03). The funding application was subject to peer review. After approving the funding for the project, the funding body had no role in the study.

Authors' contribution

AMTR, NH, PN, NG and CD obtained the funding for and designed the study on which this paper was based. AMTR led the overall project and all authors contributed to the development of the survey instrument. AMTR led the data analysis and the first draft of the paper. NH rewrote much of the introduction during review. All authors refined and approved the submitted version of the manuscript.

Conflict of interest

Alex M. T. Russell has received funding from Victorian Responsible Gambling Foundation; New South Wales Office of Responsible Gambling; the South Australian Office for Problem Gambling; Queensland Justice and Attorney-General; Gambling Research Australia; Australian Communications and Media Authority; New Zealand Ministry of Health; Australian Communications and Media Authority; National Association for Gambling Studies and the Alberta Gambling Research Institute. He has had travel expenses paid to present research by the Victorian Responsible Gambling Foundation, PsychMed and the Hawthorn Hawks Football Club. He is also affiliated with the University of Sydney and Deakin University. He declares no conflicts of interest in relation to this manuscript.

Prof. Nerilee Hing has received funding from numerous government sources, including: Gambling Research Australia, the Victorian Responsible Gambling Foundation, the Victorian Department of Justice, the South Australian Office for Problem Gambling; the NSW Responsible Gambling Fund and NSW Office of Responsible Gambling, the New Zealand Ministry of Health, the Queensland Department of Justice and Attorney-General, the South Australian Independent Gambling Authority, the ACT Gaming and Racing Commission, the Australian Research Council, Australia's National Research Organisation for Women's Safety, and the Australian Media and Communications Authority. She has also been subcontracted to assist with research projects conducted by Ogilvy Research, ORC International, First Person Consulting, and the First Nations Foundation. She declares that she has no conflicts of interest in relation to this manuscript.

Dr Philip Newall is a member of the Advisory Board for Safer Gambling – an advisory group of the Gambling Commission in Great Britain, and in 2020 was a special advisor to the House of Lords Select Committee Enquiry on the Social and Economic Impact of the Gambling Industry. In the last three years Philip Newall has contributed to research projects funded by the Academic Forum for the Study of Gambling, Clean Up Gambling, Gambling Research Australia, NSW Responsible Gambling Fund, and the Victorian Responsible Gambling Foundation. Philip Newall has received open access fee grant income from Gambling Research Exchange Ontario.

Nancy Greer has received funding from the Victorian Responsible Gambling Foundation, New South Wales Office of Responsible Gambling, Gambling Research Australia, and Australia's National Research Organisation for Women's Safety. Nancy declares no conflicts of interest in relation to this manuscript.

Dr Cassandra K. Dittman has received funding from the New South Wales Office of Responsible Gambling, and the South Australian Office for Problem Gambling. Cassandra declares no conflicts of interest in relation to this manuscript.

Dr Hannah Thorne has received funding from the Victorian Responsible Gambling Foundation, New South Wales Office of Responsible Gambling, and Gambling Research Australia. She declares no conflicts of interest in relation to this manuscript.

Prof Matthew Rockloff has received research funds from Gambling Research Australia, Victorian Responsible Gambling Foundation, Queensland Treasury, Victorian Treasury, NSW Responsible Gambling Fund, NSW Office of Liquor & Gaming, Tasmanian Department of Treasury and Finance, New Zealand Ministry of Health, Department of Families, Housing, Community Services and Indigenous Affairs, Alberta Gambling Research Institute and the First Nations Foundation.

Acknowledgements

The authors wish to thank the Victorian Responsible Gambling Foundation for funding this study, especially Rosa Billi, Kate Scalzo and Amanda Burns for their patience and support during the COVID-19 pandemic. Thanks also to the Alannah and Madeline Foundation, especially Linda Barry and Ariana Kurzeme, for their support of the project. The authors also thank the survey participants for taking part in the study, and Veronica Smith and Mallory Colys at Qualtrics for assistance with recruitment. Thanks to Professor Matthew Browne for useful comments during survey analysis, to Kristie-Lee Alfrey for assistance with survey programming and to CQUniversity for in-kind contributions. Finally, thanks to the reviewers and editor for helpful, constructive comments during peer review.

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  • Rockloff, M., Russell, A. M. T., Greer, N., Lolé, L., Hing, N., & Browne, M. (2020). Loot boxes: Are they grooming youth for gambling. CQUniversity Australia. https://austgamingcouncil.org.au/sites/default/files/2020-08/AGC%20LR%20-%20Loot%20Boxes_%20Are%20they%20grooming%20youth%20for%20gambling_.pdf.

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  • Rockloff, M., Russell, A. M. T., Greer, N., Lole, L., Hing, N., & Browne, M. (2021). Young people who purchase loot boxes are more likely to have gambling problems: An online survey of adolescents and young adults living in NSW Australia. Journal of Behavioral Addictions. https://doi.org/10.1556/2006.2021.00007.

    • Search Google Scholar
    • Export Citation
  • Russell, A. M. T., Armstrong, T., Rockloff, M., Greer, N., Hing, N., & Browne, M. (2020). Exploring the changing landscape of gambling in childhood, adolescence and young adulthood. CQUniversity. https://www.alexmtrussell.com.au/s/R14-2020-Russell-Armstrong-Rockloff-Greer-Hing-Browne-Exploring-the-changing-landscapes-of-gambling.pdf.

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    • Export Citation
  • Russell, A. M. T., Browne, M., Hing, N., Rockloff, M., & Newall, P. (2022). Are any samples representative or unbiased? Reply to pickering and blaszczynski. International Gambling Studies, 22(1). https://doi.org/10.1080/14459795.2021.1973535.

    • Search Google Scholar
    • Export Citation
  • Russell, A. M. T., Hing, N., Li, E., & Vitartas, P. (2019). Gambling risk groups are not all the same: Risk factors amongst sports bettors. Journal of Gambling Studies, 35(1), 225246. https://doi.org/10.1007/s10899-018-9765-z.

    • Search Google Scholar
    • Export Citation
  • Sakata, K., & Jenkinson, R. (2022). What is the link between video gaming and gambling? (Growing up in Australia snapshot series, issue 7). Australian Institute of Family Studies.

    • Search Google Scholar
    • Export Citation
  • Spicer, S. G., Fullwood, C., Close, J., Nicklin, L. L., Lloyd, J., & Lloyd, H. (2022). Loot boxes and problem gambling: Investigating the “gateway hypothesis. Addictive Behaviors, 131, 107327. https://doi.org/10.1016/j.addbeh.2022.107327.

    • Search Google Scholar
    • Export Citation
  • Tacon, R., & Vainker, S. (2017). Fantasy sport: A systematic review and new research directions. European Sport Management Quarterly, 17(5), 558589. https://doi.org/10.1080/16184742.2017.1347192.

    • Search Google Scholar
    • Export Citation
  • Toce-Gerstein, M., Gerstein, D. R., & Volberg, R. A. (2009). The NODS-CLiP: A rapid screen for adult pathological and problem gambling. Journal of Gambling Studies, 25(4), 541555. https://doi.org/10.1007/s10899-009-9135-y.

    • Search Google Scholar
    • Export Citation
  • Veselka, L., Wijesingha, R., Leatherdale, S. T., Turner, N. E., & Elton-Marshall, T. (2018). Factors associated with social casino gaming among adolescents across game types. BMC Public Health, 18(1), 115. https://doi.org/10.1186/s12889-018-6069-2.

    • Search Google Scholar
    • Export Citation
  • Wardle, H. (2019). The same or different? Convergence of skin gambling and other gambling among children. Journal of Gambling Studies. https://doi.org/10.1007/s10899-019-09840-5.

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    • Export Citation
  • Zendle, D., Cairns, P., Barnett, H., & McCall, C. (2020). Paying for loot boxes is linked to problem gambling, regardless of specific features like cash-out and pay-to-win. Computers in Human Behavior, 102, 181191. https://doi.org/10.1016/j.chb.2019.07.003.

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  • Zendle, D., Meyer, R., & Over, H. (2019). Adolescents and loot boxes: Links with problem gambling and motivations for purchase. Royal Society Open Science, 6(6), 190049. https://doi.org/10.1098/rsos.190049.

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  • Ziegler, M., Kemper, C. J., & Kruyen, P. (2014). Short scales – five misunderstandings and ways to overcome them. Journal of Individual Differences, 35(4), 185189. https://doi.org/10.1027/1614-0001/a000148.

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  • Armstrong, T., Rockloff, M., Browne, M., & Li, E. (2018). An exploration of how simulated gambling games may promote gambling with money. Journal of Gambling Studies, 34(4), 11651184. https://doi.org/10.1007/s10899-018-9742-6.

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  • Gainsbury, S. M., King, D., Delfabbro, P., Hing, N., Russell, A. M. T., Blaszczynski, A., & Derevensky, J. (2015). The use of social media in gambling. Gambling Research Australia https://infohub.gambleaware.org/wp-content/uploads/2016/03/grasocialmediareport.pdf.

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  • Gainsbury, S. M., Russell, A., & Hing, N. (2014). An investigation of social casino gaming among land-based and internet gamblers: A comparison of socio-demographic characteristics, gambling and co-morbidities. Computers in Human Behavior, 33, 126135. https://doi.org/10.1016/j.chb.2014.01.031.

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  • Gainsbury, S. M., Russell, A., Wood, R., Hing, N., & Blaszczynski, A. (2015). How risky is internet gambling? A comparison of subgroups of internet gamblers based on problem gambling status. New Media & Society, 17(6), 861879. https://doi.org/10.1177/1461444813518185.

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  • Hayer, T., Kalke, J., Meyer, G., & Brosowski, T. (2018). Do simulated gambling activities predict gambling with real money during adolescence? Empirical findings from a longitudinal study. Journal of Gambling Studies, 34(3), 929947. https://doi.org/10.1007/s10899-018-9755-1.

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  • Hayer, T., Rosenkranz, M., Meyer, G., & Brosowski, T. (2019). Simuliertes Glücksspiel im Internet: Ergebnisse einer quantitativen Befragung von Schülern und Schülerinnen zu Konsummustern und Risikobedingungen. Kindheit und Entwicklung, 28(2), 123133. https://doi.org/10.1026/0942-5403/a000275.

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  • Hilbrecht, M., Baxter, D., Abbott, M., Binde, P., Clark, L., Hodgins, D. C., … Williams, R. J. (2020). The conceptual framework of harmful gambling: A revised framework for understanding gambling harm. Journal of Behavioral Addictions, 9(2), 190205. https://doi.org/10.1556/2006.2020.00024.

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  • Hing, N., Browne, M., Rockloff, M., Lole, L., & Russell, A. M. T. (2022). Gamblification: Risks of digital gambling games to adolescents. The Lancet. Child & Adolescent Health, 6(6), 357359. https://doi.org/10.1016/S2352-4642(22)00124-9.

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  • Hing, N., Dittman, C. K., Russell, A. M. T., King, D. L., Rockloff, M., Browne, M., … Greer, N. (2022). Adolescents who play and spend money in simulated gambling games are at heightened risk of gambling problems. International Journal of Environmental Research and Public Health, 19(17). https://doi.org/10.3390/ijerph191710652.

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  • Hing, N., Lole, L., Thorne, H., Sproston, K., Hodge, N., & Rockloff, M. (2023). ‘It doesn’t give off the gambling vibes … it just feels like a part of the game.’ Adolescents’ experiences and perceptions of simulated gambling while growing up. International Journal of Mental Health and Addiction. https://doi.org/10.1007/s11469-023-01119-6.

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  • Hing, N., Rockloff, M., & Browne, M. (2023). A bad bet for sports fans: The case for ending the “gamblification” of sport. Sport Management Review. https://doi.org/10.1080/14413523.2023.2260079.

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  • Hing, N., Rockloff, M., Russell, A. M. T., Browne, M., Newall, P., Greer, N., … Thorne, H. (2022). Loot box purchasing is linked to problem gambling in adolescents when controlling for monetary gambling participation. Journal of Behavioral Addictions, 11(2), 396405. https://doi.org/10.1556/2006.2022.00015.

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  • Hing, N., Russell, A. M. T., Browne, M., Rockloff, M., Greer, N., Rawat, V., … Woo, L. (2021). The second national study of interactive gambling in Australia (2019--20). Gambling Research Australia.

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  • Hing, N., Russell, A. M. T., King, D., Rockloff, M., Browne, M., Greer, N., … Coughlin, S. (2021). NSW youth gambling study 2020. Office of Responsible Gambling (NSW). https://apo.org.au/node/310975.

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  • Hing, N., Russell, A. M. T., King, D. L., Rockloff, M., Browne, M., Newall, P., & Greer, N. (2023). Not all games are created equal: Adolescents who play and spend money on simulated gambling games show greater risk for gaming disorder. Addictive Behaviors, 137, 107525. https://doi.org/10.1016/j.addbeh.2022.107525.

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  • Hing, N., Russell, A., Tolchard, B., & Nower, L. (2016a). Risk factors for gambling problems: An analysis by gender. Journal of Gambling Studies, 32(2), 511534. https://doi.org/10.1007/s10899-015-9548-8.

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  • Hing, N., Russell, A. M. T., Vitartas, P., & Lamont, M. (2016b). Demographic, behavioural and normative risk factors for gambling problems amongst sports bettors. Journal of Gambling Studies, 32(2), 625641. https://doi.org/10.1007/s10899-015-9571-9.

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  • Johansson, A., Grant, J. E., Kim, S. W., Odlaug, B. L., & Götestam, K. G. (2009). Risk factors for problematic gambling: A critical literature review. Journal of Gambling Studies, 25(1), 6792. https://doi.org/10.1007/s10899-008-9088-6.

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  • Kim, H. S., Wohl, M. J. A., Gupta, R., & Derevensky, J. L. (2017). Why do young adults gamble online? A qualitative study of motivations to transition from social casino games to online gambling. Asian Journal of Gambling Issues and Public Health, 7(1), 6. https://doi.org/10.1186/s40405-017-0025-4.

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  • Kim, H. S., Wohl, M. J. A., Salmon, M. M., Gupta, R., & Derevensky, J. (2015). Do social casino gamers migrate to online gambling? An assessment of migration rate and potential predictors. Journal of Gambling Studies, 31(4), 18191831. https://doi.org/10.1007/s10899-014-9511-0.

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  • King, D. L. (2018). Online gaming and gambling in children and adolescents – normalising gambling in cyber places. Victorian Responsible Gambling Foundation. https://responsiblegambling.vic.gov.au/resources/publications/online-gaming-and-gambling-in-children-and-adolescents-normalising-gambling-in-cyber-places-479/.

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  • King, D. L., & Delfabbro, P. H. (2016). Early exposure to digital simulated gambling: A review and conceptual model. Computers in Human Behavior, 55(Part A), 198206. https://doi.org/10.1016/j.chb.2015.09.012.

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  • King, D. L., & Delfabbro, P. H. (2020). The convergence of gambling and monetised gaming activities. Current Opinion in Behavioral Sciences, 31, 3236. https://doi.org/10.1016/j.cobeha.2019.10.001.

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  • King, D. L., Delfabbro, P. H., Kaptsis, D., & Zwaans, T. (2014). Adolescent simulated gambling via digital and social media: An emerging problem. Computers in Human Behavior, 31, 305313. https://doi.org/10.1016/j.chb.2013.10.048.

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  • Kristiansen, S., Camilla, T. M., Reventlov, L. N., & Malling, A. (2018a). Playing for fun or gambling for money: A qualitative longitudinal study of digitally simulated gambling among young Danes. Young Consumers, 19(3), 251266. https://doi.org/10.1108/YC-11-2017-00750.

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  • Kristiansen, S., Trabjerg, M. C., Lauth, N. R., & Malling, A. (2018b). Playing for fun or gambling for money: A qualitative longitudinal study of digitally simulated gambling among young Danes. Young Consumers, 19(3), 251266. https://doi.org/10.1108/YC-11-2017-00750.

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  • Kristiansen, S., Trabjerg, M. C., & Reith, G. (2015). Learning to gamble: Early gambling experiences among young people in Denmark. Journal of Youth Studies, 18(2), 133150. https://doi.org/10.1080/13676261.2014.933197.

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  • Liu, K. (2019). A global analysis into loot boxes: Is it “virtually” gambling? Washington International Law Journal, 28, 763.

  • Marchica, L., Zhao, Y., Derevensky, J., & Ivoska, W. (2017). Understanding the relationship between sports-relevant gambling and being at-risk for a gambling problem among American adolescents. Journal of Gambling Studies, 33(2), 437448. https://doi.org/10.1007/s10899-016-9653-3.

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  • McCarthy, S., Thomas, S., Pitt, H., Daube, M., & Cassidy, R. (2020). “It”s a tradition to go down to the pokies on your 18th birthday’ - the normalisation of gambling for young women in Australia. Australian and New Zealand Journal of Public Health, 44(5), 376381. https://doi.org/10.1111/1753-6405.13024.

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  • Pitt, H., Thomas, S. L., & Bestman, A. (2016). Initiation, influence, and impact: Adolescents and parents discuss the marketing of gambling products during Australian sporting matches. BMC Public Health, 16(1), 112. https://doi.org/10.1186/s12889-016-3610-z.

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  • Reith, G., & Dobbie, F. (2011). Beginning gambling: The role of social networks and environment. Addiction Research & Theory, 19(6), 483493. https://doi.org/10.3109/16066359.2011.558955.

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  • Riley, B. J., Oster, C., Rahamathulla, M., & Lawn, S. (2021). Attitudes, risk factors, and behaviours of gambling among adolescents and young people: A literature review and gap analysis. International Journal of Environmental Research and Public Health, 18(3), 984. https://doi.org/10.3390/ijerph18030984.

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  • Rockloff, M., Browne, M., Greer, N., Armstrong, T., & Thorne, H. (2020). Mobile EGM games: Evidence that simulated games encourage real-money gambling. Journal of Gambling Studies, 36(4), 12531265. https://doi.org/10.1007/s10899-019-09869-6.

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  • Rockloff, M., Russell, A. M. T., Greer, N., Lolé, L., Hing, N., & Browne, M. (2020). Loot boxes: Are they grooming youth for gambling. CQUniversity Australia. https://austgamingcouncil.org.au/sites/default/files/2020-08/AGC%20LR%20-%20Loot%20Boxes_%20Are%20they%20grooming%20youth%20for%20gambling_.pdf.

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  • Rockloff, M., Russell, A. M. T., Greer, N., Lole, L., Hing, N., & Browne, M. (2021). Young people who purchase loot boxes are more likely to have gambling problems: An online survey of adolescents and young adults living in NSW Australia. Journal of Behavioral Addictions. https://doi.org/10.1556/2006.2021.00007.

    • Search Google Scholar
    • Export Citation
  • Russell, A. M. T., Armstrong, T., Rockloff, M., Greer, N., Hing, N., & Browne, M. (2020). Exploring the changing landscape of gambling in childhood, adolescence and young adulthood. CQUniversity. https://www.alexmtrussell.com.au/s/R14-2020-Russell-Armstrong-Rockloff-Greer-Hing-Browne-Exploring-the-changing-landscapes-of-gambling.pdf.

    • Search Google Scholar
    • Export Citation
  • Russell, A. M. T., Browne, M., Hing, N., Rockloff, M., & Newall, P. (2022). Are any samples representative or unbiased? Reply to pickering and blaszczynski. International Gambling Studies, 22(1). https://doi.org/10.1080/14459795.2021.1973535.

    • Search Google Scholar
    • Export Citation
  • Russell, A. M. T., Hing, N., Li, E., & Vitartas, P. (2019). Gambling risk groups are not all the same: Risk factors amongst sports bettors. Journal of Gambling Studies, 35(1), 225246. https://doi.org/10.1007/s10899-018-9765-z.

    • Search Google Scholar
    • Export Citation
  • Sakata, K., & Jenkinson, R. (2022). What is the link between video gaming and gambling? (Growing up in Australia snapshot series, issue 7). Australian Institute of Family Studies.

    • Search Google Scholar
    • Export Citation
  • Spicer, S. G., Fullwood, C., Close, J., Nicklin, L. L., Lloyd, J., & Lloyd, H. (2022). Loot boxes and problem gambling: Investigating the “gateway hypothesis. Addictive Behaviors, 131, 107327. https://doi.org/10.1016/j.addbeh.2022.107327.

    • Search Google Scholar
    • Export Citation
  • Tacon, R., & Vainker, S. (2017). Fantasy sport: A systematic review and new research directions. European Sport Management Quarterly, 17(5), 558589. https://doi.org/10.1080/16184742.2017.1347192.

    • Search Google Scholar
    • Export Citation
  • Toce-Gerstein, M., Gerstein, D. R., & Volberg, R. A. (2009). The NODS-CLiP: A rapid screen for adult pathological and problem gambling. Journal of Gambling Studies, 25(4), 541555. https://doi.org/10.1007/s10899-009-9135-y.

    • Search Google Scholar
    • Export Citation
  • Veselka, L., Wijesingha, R., Leatherdale, S. T., Turner, N. E., & Elton-Marshall, T. (2018). Factors associated with social casino gaming among adolescents across game types. BMC Public Health, 18(1), 115. https://doi.org/10.1186/s12889-018-6069-2.

    • Search Google Scholar
    • Export Citation
  • Wardle, H. (2019). The same or different? Convergence of skin gambling and other gambling among children. Journal of Gambling Studies. https://doi.org/10.1007/s10899-019-09840-5.

    • Search Google Scholar
    • Export Citation
  • Zendle, D., Cairns, P., Barnett, H., & McCall, C. (2020). Paying for loot boxes is linked to problem gambling, regardless of specific features like cash-out and pay-to-win. Computers in Human Behavior, 102, 181191. https://doi.org/10.1016/j.chb.2019.07.003.

    • Search Google Scholar
    • Export Citation
  • Zendle, D., Meyer, R., & Over, H. (2019). Adolescents and loot boxes: Links with problem gambling and motivations for purchase. Royal Society Open Science, 6(6), 190049. https://doi.org/10.1098/rsos.190049.

    • Search Google Scholar
    • Export Citation
  • Ziegler, M., Kemper, C. J., & Kruyen, P. (2014). Short scales – five misunderstandings and ways to overcome them. Journal of Individual Differences, 35(4), 185189. https://doi.org/10.1027/1614-0001/a000148.

    • Search Google Scholar
    • Export Citation
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Dr. Zsolt Demetrovics
Institute of Psychology, ELTE Eötvös Loránd University
Address: Izabella u. 46. H-1064 Budapest, Hungary
Phone: +36-1-461-2681
E-mail: jba@ppk.elte.hu

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2022  
Web of Science  
Total Cites
WoS
5713
Journal Impact Factor 7.8
Rank by Impact Factor

Psychiatry (SCIE) 18/155
Psychiatry (SSCI) 13/144

Impact Factor
without
Journal Self Cites
7.2
5 Year
Impact Factor
8.9
Journal Citation Indicator 1.42
Rank by Journal Citation Indicator

Psychiatry 35/264

Scimago  
Scimago
H-index
69
Scimago
Journal Rank
1.918
Scimago Quartile Score Clinical Psychology Q1
Medicine (miscellaneous) Q1
Psychiatry and Mental Health Q1
Scopus  
Scopus
Cite Score
11.1
Scopus
Cite Score Rank
Clinical Psychology 10/292 (96th PCTL)
Psychiatry and Mental Health 30/531 (94th PCTL)
Medicine (miscellaneous) 25/309 (92th PCTL)
Scopus
SNIP
1.966

 

 
2021  
Web of Science  
Total Cites
WoS
5223
Journal Impact Factor 7,772
Rank by Impact Factor Psychiatry SCIE 26/155
Psychiatry SSCI 19/142
Impact Factor
without
Journal Self Cites
7,130
5 Year
Impact Factor
9,026
Journal Citation Indicator 1,39
Rank by Journal Citation Indicator

Psychiatry 34/257

Scimago  
Scimago
H-index
56
Scimago
Journal Rank
1,951
Scimago Quartile Score Clinical Psychology (Q1)
Medicine (miscellaneous) (Q1)
Psychiatry and Mental Health (Q1)
Scopus  
Scopus
Cite Score
11,5
Scopus
CIte Score Rank
Clinical Psychology 5/292 (D1)
Psychiatry and Mental Health 20/529 (D1)
Medicine (miscellaneous) 17/276 (D1)
Scopus
SNIP
2,184

2020  
Total Cites 4024
WoS
Journal
Impact Factor
6,756
Rank by Psychiatry (SSCI) 12/143 (Q1)
Impact Factor Psychiatry 19/156 (Q1)
Impact Factor 6,052
without
Journal Self Cites
5 Year 8,735
Impact Factor
Journal  1,48
Citation Indicator  
Rank by Journal  Psychiatry 24/250 (Q1)
Citation Indicator   
Citable 86
Items
Total 74
Articles
Total 12
Reviews
Scimago 47
H-index
Scimago 2,265
Journal Rank
Scimago Clinical Psychology Q1
Quartile Score Psychiatry and Mental Health Q1
  Medicine (miscellaneous) Q1
Scopus 3593/367=9,8
Scite Score  
Scopus Clinical Psychology 7/283 (Q1)
Scite Score Rank Psychiatry and Mental Health 22/502 (Q1)
Scopus 2,026
SNIP  
Days from  38
submission  
to 1st decision  
Days from  37
acceptance  
to publication  
Acceptance 31%
Rate  

2019  
Total Cites
WoS
2 184
Impact Factor 5,143
Impact Factor
without
Journal Self Cites
4,346
5 Year
Impact Factor
5,758
Immediacy
Index
0,587
Citable
Items
75
Total
Articles
67
Total
Reviews
8
Cited
Half-Life
3,3
Citing
Half-Life
6,8
Eigenfactor
Score
0,00597
Article Influence
Score
1,447
% Articles
in
Citable Items
89,33
Normalized
Eigenfactor
0,7294
Average
IF
Percentile
87,923
Scimago
H-index
37
Scimago
Journal Rank
1,767
Scopus
Scite Score
2540/376=6,8
Scopus
Scite Score Rank
Cllinical Psychology 16/275 (Q1)
Medicine (miscellenous) 31/219 (Q1)
Psychiatry and Mental Health 47/506 (Q1)
Scopus
SNIP
1,441
Acceptance
Rate
32%

 

Journal of Behavioral Addictions
Publication Model Gold Open Access
Submission Fee none
Article Processing Charge 990 EUR/article for articles submitted after 30 April 2023 (850 EUR for articles submitted prior to this date)
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%.
Subscription Information Gold Open Access

Journal of Behavioral Addictions
Language English
Size A4
Year of
Foundation
2011
Volumes
per Year
1
Issues
per Year
4
Founder Eötvös Loránd Tudományegyetem
Founder's
Address
H-1053 Budapest, Hungary Egyetem tér 1-3.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 2062-5871 (Print)
ISSN 2063-5303 (Online)

Senior editors

Editor(s)-in-Chief: Zsolt DEMETROVICS

Assistant Editor(s): Csilla ÁGOSTON

Associate Editors

  • Stephanie ANTONS (Universitat Duisburg-Essen, Germany)
  • Joel BILLIEUX (University of Lausanne, Switzerland)
  • Beáta BŐTHE (University of Montreal, Canada)
  • Matthias BRAND (University of Duisburg-Essen, Germany)
  • Ruth J. van HOLST (Amsterdam UMC, The Netherlands)
  • Daniel KING (Flinders University, Australia)
  • Gyöngyi KÖKÖNYEI (ELTE Eötvös Loránd University, Hungary)
  • Ludwig KRAUS (IFT Institute for Therapy Research, Germany)
  • Marc N. POTENZA (Yale University, USA)
  • Hans-Jurgen RUMPF (University of Lübeck, Germany)

Editorial Board

  • Max W. ABBOTT (Auckland University of Technology, New Zealand)
  • Elias N. ABOUJAOUDE (Stanford University School of Medicine, USA)
  • Hojjat ADELI (Ohio State University, USA)
  • Alex BALDACCHINO (University of Dundee, United Kingdom)
  • Alex BLASZCZYNSKI (University of Sidney, Australia)
  • Judit BALÁZS (ELTE Eötvös Loránd University, Hungary)
  • Kenneth BLUM (University of Florida, USA)
  • Henrietta BOWDEN-JONES (Imperial College, United Kingdom)
  • Wim VAN DEN BRINK (University of Amsterdam, The Netherlands)
  • Gerhard BÜHRINGER (Technische Universität Dresden, Germany)
  • Sam-Wook CHOI (Eulji University, Republic of Korea)
  • Damiaan DENYS (University of Amsterdam, The Netherlands)
  • Jeffrey L. DEREVENSKY (McGill University, Canada)
  • Naomi FINEBERG (University of Hertfordshire, United Kingdom)
  • Marie GRALL-BRONNEC (University Hospital of Nantes, France)
  • Jon E. GRANT (University of Minnesota, USA)
  • Mark GRIFFITHS (Nottingham Trent University, United Kingdom)
  • Anneke GOUDRIAAN (University of Amsterdam, The Netherlands)
  • Heather HAUSENBLAS (Jacksonville University, USA)
  • Tobias HAYER (University of Bremen, Germany)
  • Susumu HIGUCHI (National Hospital Organization Kurihama Medical and Addiction Center, Japan)
  • David HODGINS (University of Calgary, Canada)
  • Eric HOLLANDER (Albert Einstein College of Medicine, USA)
  • Jaeseung JEONG (Korea Advanced Institute of Science and Technology, Republic of Korea)
  • Yasser KHAZAAL (Geneva University Hospital, Switzerland)
  • Orsolya KIRÁLY (Eötvös Loránd University, Hungary)
  • Emmanuel KUNTSCHE (La Trobe University, Australia)
  • Hae Kook LEE (The Catholic University of Korea, Republic of Korea)
  • Michel LEJOXEUX (Paris University, France)
  • Anikó MARÁZ (Humboldt-Universität zu Berlin, Germany)
  • Giovanni MARTINOTTI (‘Gabriele d’Annunzio’ University of Chieti-Pescara, Italy)
  • Astrid MÜLLER  (Hannover Medical School, Germany)
  • Frederick GERARD MOELLER (University of Texas, USA)
  • Daniel Thor OLASON (University of Iceland, Iceland)
  • Nancy PETRY (University of Connecticut, USA)
  • Bettina PIKÓ (University of Szeged, Hungary)
  • Afarin RAHIMI-MOVAGHAR (Teheran University of Medical Sciences, Iran)
  • József RÁCZ (Hungarian Academy of Sciences, Hungary)
  • Rory C. REID (University of California Los Angeles, USA)
  • Marcantanio M. SPADA (London South Bank University, United Kingdom)
  • Daniel SPRITZER (Study Group on Technological Addictions, Brazil)
  • Dan J. STEIN (University of Cape Town, South Africa)
  • Sherry H. STEWART (Dalhousie University, Canada)
  • Attila SZABÓ (Eötvös Loránd University, Hungary)
  • Ferenc TÚRY (Semmelweis University, Hungary)
  • Alfred UHL (Austrian Federal Health Institute, Austria)
  • Róbert URBÁN  (ELTE Eötvös Loránd University, Hungary)
  • Johan VANDERLINDEN (University Psychiatric Center K.U.Leuven, Belgium)
  • Alexander E. VOISKOUNSKY (Moscow State University, Russia)
  • Aviv M. WEINSTEIN  (Ariel University, Israel)
  • Kimberly YOUNG (Center for Internet Addiction, USA)

 

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