1800 words essay 10 hrs

Computers in Human Behavior 31 (2014) 305–313

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Computers in Human Behavior

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / c o m p h u m b e h

Adolescent simulated gambling via digital and social media: An emerging problem

0747-5632/$ - see front matter � 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.chb.2013.10.048

⇑ Corresponding author. Address: School of Psychology, Level 4, Hughes Building, The University of Adelaide, Adelaide, SA 5005, Australia. Tel.: +61 8 83133740; fax: +61 8 8303 3770.

E-mail address: [email protected] (D.L. King).

Daniel L. King ⇑, Paul H. Delfabbro, Dean Kaptsis, Tara Zwaans School of Psychology, The University of Adelaide, Australia

a r t i c l e i n f o a b s t r a c t

Article history: Available online 20 November 2013

Keywords: Convergence Pathological gambling Social media Adolescence Addiction

Recently, there has been significant expansion in the range of gambling activities supported by digital technology. The convergence of gambling and digital media is of particular concern with respect to the immense potential for earlier age of gambling involvement, and development of positive attitudes and/or behavioral intentions toward gambling. This study examined the prevalence of adolescent involvement in a range of digital and social media gambling activities, and the association between expo- sure to, and involvement in, simulated gambling and monetary gambling and indicators of pathological gambling risk. A total of 1287 adolescents aged 12–17 years were recruited from seven secondary schools in Adelaide, South Australia. The results indicated that a significant proportion of young people engage in a range of simulated gambling activities via internet gambling sites, social media, smartphone applica- tions, and video-games. A logistic regression analysis showed that adolescents with a history of engage- ment in simulated gambling activities appear to be at greater risk of endorsing indicators of pathological gambling. These findings highlight the need for further research on the potential risks of early exposure to simulated gambling activities, as well as greater consideration of the need for regulation and monitor- ing of gambling activity via digital technologies.

� 2013 Elsevier Ltd. All rights reserved.

1. Introduction

1.1. Gambling and digital technology

In the last decade, there has been significant expansion in the range of gambling activities supported by digital technology (Grif- fiths & Parke, 2010; King, Delfabbro, & Griffiths, 2010). The ‘‘con- vergence’’ (Griffiths, King, & Delfabbro, 2013) of gambling and digital media is of particular interest and concern to researchers, regulators, and allied health practitioners because of its potential to increase the likelihood of young people developing an interest in gambling at an younger age (Derevensky, Sklar, Gupta, & Mes- serlian, 2010; King et al., 2010; Griffiths, King, & Delfabbro, 2012; Phillips, Ogeil, & Blaszczynski, 2012). Although some earlier re- search suggested youth participation rates in online gambling activities are usually lower than for terrestrial forms of gambling (Griffiths & Wood, 2007; Ipsos, 2009; Najman, Allen, Madden, & Brooks, 2008), more recent market research data (e.g., Casual Connect., 2012; Church-Sanders, 2011) suggest that the popularity of online gambling is increasing rapidly. This growth has led to concerns about the potential negative impacts on young people,

given that the ubiquity of these new activities allows them to gam- ble more covertly and unrestrictedly than was the case before (Flo- ros, Siomos, Fisoun, & Geroukalis, 2013).

Although much has been written about the increasing perva- siveness of monetary gambling on digital media, another less recognised concern relates to the growth of simulated gambling, or gambling without the possibility of monetary reward (King et al., 2010; Griffiths, King, & Delfabbro, 2012). Simulated gambling may be defined as a digitally simulated interactive gambling activ- ity that does not directly involve monetary gain but is otherwise structurally identical to the standard format of a gambling activity due to its wagering features and chance-determined outcomes of play. Although the boundaries between gambling and video-gam- ing are becoming increasingly blurred (for example, gaming fea- tures may be found in some gambling-like activities, and vice versa), simulated gambling may be distinguished from many forms of video-gaming (e.g., shooting action games, role-playing games) because in video-games there is a clear relationship between player strategy or actions and outcomes. Simulated gambling is a continually evolving mode of gambling that encompasses free- to-play gambling games using virtual credits, smartphone and so- cial media apps, and hybrid video-game/gambling activities with monetisation features such those found in MMOs like Runescape (Delfabbro, King, Lambos, & Puglies, 2009; Griffiths & Wood, 2010; Johansson & Gotestam, 2004). Some of these activities

306 D.L. King et al. / Computers in Human Behavior 31 (2014) 305–313

(e.g., gambling apps on Facebook) may be considered financial be- cause they allow players to purchase extra credit using real money, but they do not enable the player to ‘cash out’ winnings. Simulated gambling activities generally feature no age restriction or barriers to entry (King et al., 2010), employ inflated profit rates (Sevigny, Cloutier, Pelletier, & Ladouceur, 2005), and are presented as youth-friendly entertainment (Griffiths & Parke, 2010). Further, the emergence of gambling on video-gaming platforms has blurred the structural boundaries between gambling and gaming activities (Griffiths, 2008; Harper, 2007; King, Delfabbro, Derevensky, & Grif- fiths, 2012). For example, many forms of online video gambling or social media sites feature gambling, often for credits or points paid for with real money, and many internet gambling providers offer free-play games that are rather like video games.

1.2. The risks of simulated gambling in adolescence

Potential problems related to simulated gambling may be par- ticularly germane to young people for several reasons. The first is that young people are, by definition, developmentally immature and not always able to appraise the riskiness of activities, including gambling (Delfabbro, Lambos, King, & Puglies, 2009; Hardoon & Derevenksy, 2002; Volberg, Gupta, Griffiths, Olasson, & Delfabbro, 2010). Second, young people are particularly avid and savvy con- sumers of digital media and online services, including video games, laptops, tablets, and smartphones. Large-scale studies suggest that the average Australian adolescent spends about five hours per day engaged in digital media activities, including 2.5 h using the Inter- net (Australian Communications & Media Authority, 2007, 2008). Most youth use Facebook to communicate and post information, browse wikis, video tutorials, and other forums to create, gather, and share information, and visit sites such as eBay to buy and sell goods. Many acquired skills and knowledge of web functionality and online navigation may be transferable to use of online gambling activities and features. Additionally, the significant amount of lei- sure time spent on the Internet suggests there is significant poten- tial for exposure to gambling promotions, online gambling activities, and assorted incentives to gamble (McMullan & Kervin, 2012; Messerlian, Byrne, & Derevensky, 2004; Monaghan, Dereven- sky, & Sklar, 2008). Third, young people are often influenced by psy- chological and social factors (e.g., peer group pressure, the desire to conform, disillusionment, depression, low self-esteem, poor emo- tion regulation) that make isolated technology-based activities par- ticularly attractive to them Potenza et al. (2011). Current national and international evidence confirms that many young people expe- rience problems associated with online technology use (Ferguson, Coulson, & Barnett, 2011; Gentile, 2009; King, Delfabbro, Griffiths, & Gradisar, 2011; King, Haagsma, Delfabbro, Gradisar, & Griffths, 2013b; Kuss, Griffiths, & Binder, 2013; Sletten,Torgersen, von Soest, Frøyland, & Hansen, 2010). Although the risks of excessive online social networking and video-gaming are well-documented, less re- search has examined whether simulated gambling activities can give rise to similar social and psychological problems.

1.3. Research on adolescent gambling

Research studies of young people aged under 18 years suggest that between 50% and 70% gamble at least once per year and that between 1% and 4% display behaviors consistent with a gambling pathology (Delfabbro, 2012; Hardoon & Derevenksy, 2002). Patho- logical gambling is usually associated with poorer social relation- ships and psychological functioning; a greater likelihood of involvement in other high risk behaviors; and, poorer educational performance (Delfabbro & King, 2012). Adolescents with gambling problems are more likely to have peers and family who gamble,

have unrealistic views about the nature of gambling, and a history of gambling problems in their immediate family (Delfabbro, 2012).

An emerging but limited body of research suggests that simu- lated gambling may co-occur with monetary gambling activity. To date the largest study of simulated gambling among youth has been conducted by Ipsos (2009), who surveyed 8598 adolescents about their gambling and ‘gambling-like’ behavior. Over 25% of adoles- cents had played in ‘money-free’ mode of gambling in the week pre- ceding the survey, with opportunities on social networking sites four times more popular than those presented on real gambling sites. Although the design of the study precluded statements of cau- sality, simulated gambling behavior was the strongest predictor of monetary gambling, and also significantly predicted at-risk gam- bling. Comparable findings have been reported in other studies (By- rne, 2004; Griffiths & Wood, 2007; Hardoon, Derevensky, & Gupta, 2002). Griffiths and Wood (2007) surveyed 8017 adolescents aged 12–15 years, and reported that 29% of adolescents who had gam- bled online also reported playing the free ‘demo’ games. Byrne (2004) reported that young people with gambling problems were significantly more likely to report online simulated gambling in the past year than those without gambling problems. Hardoon et al. (2002) reported that 25% of youth with serious gambling prob- lems and 20% of those at-risk for a gambling problem reported play- ing online using practice/trial sites. Similarly, research on adult gamblers conducted by McBride and Derevensky (2009) reported that 77% of online gamblers (N = 563) reported playing ‘gambling- like’ games (e.g., practice modes) in addition to monetary gambling on the Internet. Overall, it may be observed that the literature on youth simulated gambling is limited by: (a) its age of publication (i.e., older findings may not accurately reflect the current status of youth gambling given changes in the technological and social con- text of gambling), (b) the lack of studies conducted outside of the UK, and (c) the lack of detailed examination of a range of gambling activities available through digital and social media.

1.4. The current study

The vulnerability of adolescents may place them at greater risk of problematic patterns of gambling via new and emerging digital and social media. On the one hand, it has been proposed that early exposure to gambling activities may condition a range of ‘‘safer’’ responses to gambling stimuli (e.g., smaller bet sizes, infrequent/ social play), or develop knowledge about the chance-determined nature of gambling, including the belief that one is very unlikely to win in the long-term. As Najman et al. (2008) state:

Practice play can affect the appeal of gambling games by remov- ing some of the mystery and excitement that surrounds previ- ously unobtainable casino type games. By experimenting with simulated casino games young people become accustomed to them and become easily bored.

However, an alternative view is that simulated gambling activ- ities may facilitate the transition to monetary forms of gambling (McBride & Derevensky, 2009), and/or develop a behavioral ten- dency toward sustained gambling activity and riskier gambling strategies (Bednarz, Delfabbro, & King, 2013). The research litera- ture on gambling convergence is currently limited with regard to explaining how and to what extent adolescent gamblers may be- come involved in these new forms of gambling and gambling-like activities. However, it is well-documented that online gambling service providers employ numerous strategies and techniques out- side the scope of current regulation to entice young players to ini- tiate and develop a familiarity with gambling (Derevensky et al., 2010; McBride & Derevensky, 2009; McMullan, Miller, & Perrier, 2012).

D.L. King et al. / Computers in Human Behavior 31 (2014) 305–313 307

Although there is currently no established theoretical model for conceptualising risks of simulated gambling among youth, expert commentary and limited research evidence, as summarised above, suggests that simulated gambling in adolescence may act as a ‘‘gateway’’ activity that grooms a young person toward transition to higher-risk, monetary gambling activities. To examine this pos- sibility, this study aimed to assess: (a) the prevalence of adolescent involvement in a range of digital and social media gambling activ- ities, (b) the extent of the cross-over or association between simu- lated gambling and monetary gambling activities, and (c) whether simulated gambling exposure was associated with indicators of pathological gambling risk.

2. Method

2.1. Design

This study employed a cross-sectional survey design. Fifty sec- ondary schools in the outer metropolitan region of Adelaide, South Australia, were randomly selected from a comprehensive list of public and private schools. Catholic schools were excluded due to barriers in obtaining ethical clearance. Each school principal was sent a letter and one-week follow-up email invitation to partici- pate. The study was promoted as an investigation of ‘‘electronic media use and mental health in young people’’ (see King, Delfab- bro, Zwwans, & Kaptsis, 2013a). Each participating school was pro- vided with an individualised summary report of findings, which included an indication of the number of adolescents at-risk of men- tal health problems. In total, seven co-educational schools (4 pub- lic, 3 private) provided consent to participate. Remaining schools either declined to participate (N = 20) or did not respond to the invitations (N = 23). Data were collected from June to August 2012.

All participants provided informed consent and were free to withdraw from the study at any time. The study was conducted at each secondary school during class hours. Three of the authors (DLK, DK, and TZ) facilitated data collection at each of the second- ary schools. Upon obtaining consent, a teacher administered the questionnaire to each student in the classroom. An online version of the questionnaire was available via Survey Monkey for those schools with the requisite IT infrastructure. Completed surveys were compiled and analyzed using SPSS for Windows (v18.0). A to- tal of 73 responses were excluded due to erroneous responses or missing data. This study was approved by the Human Research Ethics Subcommittee at the University of Adelaide, and the Depart- ment for Education and Child Development.

2.2. Participants

A total of 1287 high school students aged 12–17 years were re- cruited. The gender distribution was 49.6% male and 50.4% female. The mean age was 14.9 years (SD = 1.5). Participants identified as Caucasian Australian (85.5%), Asian (6.8%), European (5.1%), Aboriginal (1.6%), or Other (.9%). English was the primary language spoken at home by 95% of participants. Rates of ownership and/or home accessibility for various electronic media device were as fol- lows: mobile phone or smartphone (91%), portable music player (89%), laptop (86%), video-gaming console (78%), personal com- puter (71%), and tablet devices (37%). The mean age at which ado- lescents had first used various electronic media devices included: (i) the Internet at 8.2 years old (SD = 2.3) (ii) video-games at age of 9.2 years (SD = 3.7), and (iii) mobile phone at age of 10.9 years (SD = 2.1).

Table 1 presents a summary and chi-square analysis of demo- graphic differences according to ‘at-risk’ gambling status. At-risk gambling referred to endorsing at least 1 indicator of pathological

gambling. This classification method was consistent with classifi- cation employed in other studies of adolescent gamblers (e.g., Del- fabbro & Thrupp, 2003). Although this inclusive classification method may potentially over-classify cases where gambling is un- likely to be a significant issue, this method is often used in evalu- ating risk in youth mental health settings where low specificity and higher sensitivity are prioritized. For example, a youth who re- ports fleeting thoughts of self-harm may not be considered to be at significant risk of suicide (or warrant a clinical diagnosis), however endorsement of this indicator may often be considered ‘at-risk’. The results indicated that males and those participants of Cauca- sian Australian background were significantly more likely to en- dorse items related to pathological gambling, but these observed effects were quite small according to Cohen’s (1992) guidelines. Gender and ethnicity were included as covariates in subsequent multivariate analysis to account for the potentially confounding role of these variables.

2.3. Materials

A standardised questionnaire assessed basic demographic infor- mation (i.e., age, sex, school grade, cultural background, main lan- guage spoken at home), and aspects of electronic media use (i.e., ownership and accessibility, frequency of use of each device in a typical week period over the previous 3-month period, function and social context of media use, and age at which devices were first used). Additional questionnaires assessed gambling behavior across a range of activities, as well as pathological gambling and mental health indicators.

2.3.1. Simulated and monetary gambling Gambling activity was assessed by a 25-item questionnaire that

included questions about gambling with and without money. Gi- ven the lack of guiding literature on assessment of gambling across a range of land-based (e.g., casino, public house) and digital or on- line (e.g., mobile phone, social media) environments, this question- naire was designed for the purpose of this study. However, some of its content was based on questions employed by Ipsos (2009) and Griffiths and Wood (2007). Adolescents indicated the frequency of involvement in the following gambling activities in the previous 12 months: card games (e.g., blackjack, poker, etc.), electronic gaming machines, wagering on races or sports, lotteries, scratch cards, or any other activity (i.e., ‘‘other’’). Frequency was assessed by a 5-point scale including: 1 = never, 2 = once or twice a year, 3 = three times a year to monthly, 4 = two or three times per month, and 5 = weekly. For each activity, participants indicated whether they had: (1) played with money (i.e., financial gambling), (2) played without money involved (i.e., simulated gambling), and (3) for relevant activities (e.g., cards, gaming machines) whether they gambled via the Internet. For example, when asked to indicate involvement in card or table tables (item 1), participants indicated their frequency of involvement (using the 5-point scale) for mone- tary and then simulated play, and also whether this activity oc- curred online.

Further questions assessed historical involvement in online financial gambling and simulated gambling activities. One item asked participants to indicate if they had ever tried to gamble with money on the Internet. Additional items asked whether the participant had ever tried simulated gambling via: (1) free-play or ‘practice’ modes online, (2) social networking site applications (e.g., Zynga Poker), (3) smart phone apps (e.g., Slotomania), (4) video-game simulations or online games (e.g., Runescape). These items were scored using a dichotomous (yes/no) format. For each question, participants were asked to report the name of the game, website, or application, if able to recall. Adolescents were asked to indicate the proportion of their Internet time that was spent

Table 1 Demographic information of the total sample of adolescents according to risk level of problem gambling.

N Total % No problem gambling (N = 910) At-risk/problem gambling (N = 304) Group differences Effect size

n % n % v2 (df = 1) Sig. U

Sex/gender Male 602 49.6 413 68.6 189 31.4 Female 612 50.4 497 81.2 115 18.8 25.7 .001 0.15

Grade Junior (7–9) 580 47.8 446 76.9 134 23.1 Senior (10–12) 634 52.2 464 73.2 170 26.8 2.2 .136 0.04

School Public 637 52.5 475 74.6 162 25.4 Private 577 47.5 435 75.4 142 24.6 0.1 .741 0.01

Ethnicity Caucasian Australian 1038 85.5 796 64.8 242 35.2 Other 176 14.5 114 76.7 62 23.3 11.3 .001 0.10

Media accessibilitya

1–2 devices 398 32.8 307 77.1 91 22.9 3+ devices 816 67.2 603 73.9 213 26.9 1.4 .221 0.03

NB: Percentages refer to group/at risk ⁄ 100. a Refers to access to electronic devices with online and simulated gambling capabilities (i.e., personal computer, laptop, mobile phone, and tablet).

308 D.L. King et al. / Computers in Human Behavior 31 (2014) 305–313

involved in gambling activities, and the number of video-games that they currently played which featured simulated online gam- bling. A copy of this measure is available by request to the corre- sponding author.

2.3.2. Pathological gambling The Diagnostic Statistical Manual-IV-Multiple Response Format

for Juveniles (DSM-IV-MR-J) is a 10-item tool that assesses patho- logical gambling among youth (American Psychiatric Association, 2000). The scale examines the following dimensions of pathologi- cal gambling: cognitive salience, loss of control, escape, lies and se- crecy, chasing losses, tolerance, withdrawal, familial conflict, and school absenteeism. Items referred to financial gambling only. All items were scored using a 4-point Likert scale (1 = never, 2 = some- times, 3 = often, 4 = frequently) to enable greater sensitivity to infrequent and/or emerging pathological gambling behaviors. Although there has been long been debate regarding the validity of assessment tools for youth gambling problems (see Derevensky, Gupta, & Winters, 2003; Ladouceur et al., 2000), this study’s ap- proach was consistent with large youth prevalence studies (e.g., the British Survey of Children and Gambling). Adolescents who re- sponded to at least one pathological gambling criteria with ‘‘often’’ were classified as ‘at-risk’, whereas endorsing 5 or more criteria was indicative of ‘probable pathological’ gambling status (Der- evensky & Gupta, 2006). Internal consistency of the measure in the current study was high (Cronbach’s alpha = .90).

2.3.3. Mental health status The Revised-Children Anxiety Depression Scale (RCADS) is a

widely used instrument for assessing children’s symptoms corre- sponding to DSM-IV anxiety and major depressive disorders (Chor- pita, Yim, Moffitt, Umemoto, & Francis, 2000). The 47-item scale yields scores on six subscales: Separation Anxiety Disorder, Social Phobia, Obsessive Compulsive Disorder, Panic Disorder, General- ised Anxiety Disorder, and Major Depressive Disorder. Raw scores on each subscale are converted to T-scores (i.e., scores standard- ised according to gender and age). T-scores indicate normal (<65), borderline (65–69), or clinically significant (70+) symptom- atology. The RCADS has demonstrated sound psychometric proper- ties in the Australian population (De Ross, Gullone, & Chorpita, 2002).

3. Results

3.1. Prevalence of simulated gambling

The first aim of this study was to examine the prevalence of adolescent involvement in simulated gambling. Table 2 presents a summary of adolescents’ involvement across a range of digital and social media-based simulated gambling activities in the last 12 months. The most popular type of simulated gambling was on- line card games (11.9%), followed by electronic gaming machines (3.8%), and sports betting activities (3.2%). A chi-square analysis of non-pathological versus at-risk/pathological (henceforth ‘at- risk’) gamblers indicated that all types of simulated gambling were significantly more prevalent than would be expected among at-risk gamblers. About 1 in 4 at-risk gamblers engaged in simulated casi- no card games (frequency [% of group]: once or twice a year [38%], three times a year to monthly [28%], two or three times per month [22%], and weekly [11%]), and 1 in 10 reported playing simulated electronic slot machine games (frequency [% of group]: once or twice a year [54%], three times a year to monthly [14%], two or three times per month [14%], and weekly [17%]). Overall, at-risk adolescent gamblers reported rates of participation in simulated gambling activities at approximately 3 times or greater than their non-pathological gambling counterparts. The size of observed ef- fects was small to moderate (Cohen, 1992).

A substantial proportion of the sample (31.5%) reported past involvement in at least one simulated gambling activity. About 25% of adolescents reported to have engaged in simulated gam- bling in a video game, either as bonus feature of the video game or as a virtual gambling experience (see King, Delfabbro, et al., 2012). The majority of participants did not provide qualitative feedback indicating specific simulated gambling activities. The lim- ited data highlighted the following games as containing simulated gambling: Grand Theft Auto (n = 35), Red Dead Redemption (n = 16), Xbox Live Poker (n = 10), Pokemon (n = 11), Runescape (n = 7), Fable 2 (n = 5), and Fallout: New Vegas (n = 3). To a lesser extent, adoles- cents reported engaging in simulated gambling via Facebook, with the most commonly identified applications being Zynga Poker (n = 28) and Texas Hold’Em Poker (n = 9). Fewer adolescents re- ported to have engaged in simulated gambling via smartphone apps (6.3%) and free-play or ‘demo’ modes of casino websites (4.7%). The most frequently reported smartphone app was Slotoma- nia (n = 15), although several participants reported ‘‘iPhone games’’

Table 2 Current and historical involvement in simulated gambling activities according to risk level of problem gambling.

Simulated gambling N % No Problem gambling (N = 910) At-risk or problem gambling (N = 304) Group differences Effect size

n % of Group n % of Group v2 (df = 1) Sig. U

Current usea

Card games 145 11.9 71 7.8 74 24.3 59.3 .001 0.22 EGMs 46 3.8 14 1.5 32 10.5 53.6 .001 0.21 Sports betting 39 3.2 18 2.0 21 6.9 20.9 .001 0.13 Racing 24 2.0 9 1.0 15 4.9 18.3 .001 0.12 Other 29 2.4 10 1.1 19 6.2 25.9 .001 0.15

Historical use Free play or demo modes 55 4.7 16 1.8 39 13.3 64.8 .001 0.24 Facebook apps 117 9.6 48 5.5 69 23.5 79.9 .001 0.26 Smartphone apps 77 6.3 24 2.7 53 18.1 84.3 .001 0.27 Video-game features 314 25.9 179 20.4 135 46.1 73.8 .001 0.25

a Refers to activity in the past 12 months.

D.L. King et al. / Computers in Human Behavior 31 (2014) 305–313 309

which may have included this app. Adolescents tended to report accessing free-play casino activities via the website Pokerstars (www.pokerstars.com) (n = 6). A chi-square analysis examining nor- mal versus at-risk gamblers indicated that a history of involvement in all types of simulated gambling was significantly more prevalent among at-risk gamblers as compared to non-pathological gam- blers. Rates of simulated gambling via smartphone apps were over 6 times more prevalent among at-risk gamblers than non- patho- logical gamblers. The size of these effects was small to moderate.

3.2. Co-occurrence of simulated gambling and monetary gambling

The second aim of this study was to measure the association be- tween simulated gambling and monetary gambling activities. Ta- ble 3 presents a summary of participants’ monetary gambling activities and features of pathological gambling, according to level of involvement in simulated gambling. In the overall sample, the most prevalent types of monetary gambling were scratch tickets (15.3%), card games (9.4%), and wagering on races (10.4%). Fewer adolescents (2.3%) reported to have had tried any form of gambling directly involving money on the Internet at least once in the past. A

Table 3 The co-occurrence of simulated gambling and monetary gambling activity.

N % No simulated gambling (N = 1050)

n % of Group

Monetary gamblinga

Card games 114 9.4 79 7.5 EGMs 40 3.3 25 2.4 Scratch tickets 186 15.3 122 11.6 Racing 126 10.4 72 6.9 Lottery 66 5.4 34 3.2 Sports betting 69 5.6 40 3.8

Pathological gambling Preoccupation/Intention 245 20.2 179 17.0 Spent more than intended 75 6.2 48 4.6 Withdrawal 47 3.9 34 3.2 Gambling to escape 50 4.7 29 2.8 Steal to fund gambling 56 4.6 37 3.5 Missing school 44 3.6 27 2.6 Lies/secrecy 35 2.9 20 1.9 Arguments 42 3.5 28 2.7 Problem gambling statusb 12 1.0 7 0.7

Mental healthc

RCADS Anxiety 228 18.8 197 18.8 RCADS Depression 297 24.5 255 24.3

a Refers to activity in the past 12 months. b Endorsement of four or more criteria on the DSM-IV-MR-J. c Meeting the borderline or clinical cut-off. NS: Non-significant.

chi-square analysis indicated that involvement in monetary gam- bling were significantly more prevalent than would be expected among simulated gamblers, v2(1) = 86.1, p < .05, Eta = .27. Rates of monetary gambling via card games and scratch tickets were 3 times more prevalent than predicted among simulated gamblers, whereas wagering on races was 5 times more prevalent, as com- pared to adolescents who did not engage in simulated gambling. The size of observed effects was small to moderate.

The prevalence of probable pathological gambling in the overall sample was 1.0%. This was lower than recent studies of Australian adolescents (Delfabbro & King, 2011; Delfabbro, Lahn, & Grabosky, 2005; Delfabbro, Lambos, et al., 2009) but may reflect the broader age range employed in this study and also the timing of the survey. In recent years, prevalence rates for pathological and problem gambling in Australia have declined over the last decade (Delfab- bro, 2012; Productivity Commission, 2010). Given this lower than expected base rate of pathological gambling, all between-group analyses assessed group membership as defined by ‘at-risk’ gam- bling, or endorsing at least 1 indicator of pathological gambling. Inspection of individual pathological gambling indicators indicated that symptom 1 (i.e., preoccupation with gambling and future

Simulated gamblers (N = 164) Group differences Effect size

n % of Group v2 (df = 1) Sig. U

35 21.3 31.8 .001 0.16 15 9.1 20.3 .001 0.13 64 39.0 82.1 .001 0.26 54 32.9 103.6 .001 0.29 32 19.5 73.1 .001 0.25 29 17.7 50.9 .001 0.21

66 40.2 47.4 .001 0.20 27 16.5 34.6 .001 0.17 13 7.9 8.3 .004 0.08 21 12.8 36.2 .001 0.17 19 11.6 21.0 .001 0.13 17 10.4 24.6 .001 0.14 15 9.1 26.5 .001 0.15 14 8.5 14.6 .001 0.11

5 3.0 8.2 .004 0.02

31 18.9 .01 NS 0.01 42 25.6 13 NS 0.01

310 D.L. King et al. / Computers in Human Behavior 31 (2014) 305–313

intention to gamble) was the most endorsed indicator of patholog- ical gambling, as reported by 1 in 5 adolescents in the sample. Chi- square analysis indicated that all pathological gambling indicators were significantly more prevalent than expected among simulated gamblers than those not involved in simulated gambling. All ob- served effects were small to moderate in size. Notably, a preoccu- pation with gambling was reported by 40% of the simulated gamblers, as compared to 20% of adolescents who were not cur- rently involved in simulated gambling. No significant differences in rates of depression and anxiety were observed between those who did and those who did not engage in simulated gambling.

3.3. Simulated gambling as a predictor of at-risk/pathological gambling

The final of aim of this study was to assess whether simulated gambling exposure was associated with endorsement of indicators of pathological gambling. Table 4 presents a series of Spearman’s rank order correlations to assess bivariate relationships between indicators of current and historical simulated gambling, monetary gambling, and at-risk gambling. This analysis identified several sta- tistically significant relationships between simulated gambling and monetary gambling. The simulated gambling factor with the stron- gest positive association with monetary gambling was the number of simulated gambling activities engaged in within the preceding 12-month period. The simulated gambling factor with the stron- gest positive association with pathological gambling score was past exposure to simulated gambling activities (i.e., Facebook and smartphone apps, and gambling video-games). The strength of this relationship was comparable to the association between current monetary gambling activity and at-risk gambling status. Mental health factors (depression and anxiety) were not significantly asso- ciated with simulated gambling indicators.

A logistic regression was conducted with at-risk gambling sta- tus as a binary outcome variable (i.e., endorsing an indicator of pathological gambling versus no endorsement of indicators) and the main effects of gender, ethnicity, monetary gambling, and sim- ulated gambling (current and historical use) as predictors. The model presented in Table 5 significantly predicted pathological gambling (v2(5) = 216.24, p < .01) and explained 25% of the vari- ance in pathological gambling scores (Nagelkerke’s R2 = .245). The number of monetary gambling activities was predictive of at-risk gambling (B = .6, Wald v2(1) = 34.6, p < .01). Every unit increase in number of monetary gambling activities increased the odds of at-risk gambling by 157%. Adolescents who endorsed at least one pathological gambling indicator reported significantly more mone- tary gambling activities (M = 1.06, SD = 1.51) than those adoles- cents who endorsed no pathological gambling criteria (M = .31, SD = .75).

Exposure to or past involvement in simulated gambling activi- ties was a significant predictor of pathological gambling risk

Table 4 Correlations between simulated gambling, monetary gambling, and problem gambling va

Variable 2 3

1. Simulated gambling: frequency .83* .29*

2. Simulated gambling: number of activities .26*

3. History of simulated gambling: number of activities 4. Current Internet gamblinga

5. Monetary gambling: frequency 6. Monetary gambling: number of activities 7. DSM-IV-MR-J score 8. RCADS anxiety 9. RCADS depression

a Refers to the percentage of time spent on gambling activities on the Internet each w * <0.01.

(B = .73, Wald v2(1) = 63.6, p < .01), and was the strongest overall predictor in the model. Every unit increase in number of past expe- riences with simulated gambling activities increased the odds of at-risk gambling by 208%. Adolescents who endorsed at least one pathological gambling indicator reported a past involvement in significantly more simulated gambling activities (M = .99, SD = 1.14) than those adolescents who endorsed no pathological gambling criteria (M = .30, SD = .62). Current simulated gambling activity was a significant but considerably weaker predictor of pathological gambling criteria, (B = .24, Wald v2(1) = 10.1, p < .01), with each unit increase in number of current simulated gambling activities increasing the odds of at-risk gambling by 128%. Gender and age were not significant predictors in the model.

4. Discussion

This study examined the potential risks of simulated gambling via digital and social media in terms of its association with mone- tary gambling and pathological gambling. The results indicated that simulated gambling is a popular activity among young people aged 12–18 years, with 13% of the sample reporting involvement in simulated gambling in the last 12 months, and 32% reporting at least one lifetime episode of involvement in one or more types of simulated gambling activity. The most popular types were online card games, electronic gaming machines, and sports betting activ- ities. About 1 in 4 at-risk adolescents had engaged in simulated ca- sino card games, and 1 in 10 reported playing simulated electronic slot machine games. Simulated gambling activities were 3 or more times popular among those adolescents who endorsed items on a measure of pathological gambling. The most commonly reported experience of past exposure to simulated gambling was gambling in video games, including best-selling video-games such as Grand Theft Auto and Pokemon. About 1 in 10 adolescents reported to have tried gambling apps on Facebook (e.g., Zynga Poker), and 1 in 20 adolescents had tried gambling applications on a smartphone (e.g., Slotomania). These findings provide needed quantitative data on the nature and extent of youth involvement in gambling on the Internet and forms of digital media.

This study provides further evidence that youth can access and do engage in monetary gambling activities despite established bar- riers to entry. Consistent with other youth gambling studies con- ducted in Australia (Delfabbro & King, 2011; Delfabbro, King, et al., 2009), the most prevalent types of monetary gambling were scratch tickets (15.3%), card games (9.4%), and wagering on races (10.4%), which may be associated with specific cultural events (e.g., the Melbourne Cup). Although significantly fewer adolescents (2.3%) reported to have tried gambling with money on the Internet, the continuing expansion of Internet gambling may lead to greater uptake and involvement among youth. As predicted, monetary gambling was significantly more prevalent among simulated

riables.

4 5 6 7 8 9

.29* .21* .33* .31* .02 .03

.20* .37* .38* .30* .01 .02

.21* .25* .23* .35* .03 .10 .19* .19* .26* .05 .06

.84* .37* .05 .08 .33* .06 .07

.13* .15*

.81*

eek.

Table 5 Logistic regression of demographic and gambling factors predicting ‘at-risk’ gambling status.

B SE Wald Sig. Odds ratio 95% CI

Lower Upper

Constant �1.50 .76 Gender �.22 .15 1.9 .16 .80 .60 1.10 Ethnicity .19 .09 4.1 .04 1.22 1.01 1.48 Age .02 .05 0.2 .63 .97 .88 1.08 Monetary gambling: Current activities .46 .08 34.6 <.01 1.57 1.40 1.84 Simulated gambling: Current activities .24 .08 10.1 <.01 1.28 1.10 1.49 Simulated gambling: History of activities .73 .09 63.6 <.01 2.08 1.73 2.48

D.L. King et al. / Computers in Human Behavior 31 (2014) 305–313 311

gamblers than would be expected given base rate data. Card games and scratch tickets were 3 times more prevalent among simulated gamblers as compared to adolescents who did not engage in simu- lated gambling. Although this finding may indicate that youth who engage in simulated gambling are at greater risk of gambling with money, it may also indicate that certain individual and familial fac- tors underlie both simulated and monetary gambling. This study should therefore be considered as a preliminary investigation, with prospective longitudinal designs needed to examine the possible pathway from simulated gambling during adolescence to mone- tary gambling in early adulthood with greater precision.

Unexpectedly, very few pathological gamblers (1%) were identi- fied in this relatively large study (N > 1200). Therefore our analysis plan assessed simulated gambling as a predictor of potentially ris- ky gambling behavior (i.e., meeting at least one indicator of path- ological gambling). The results showed that adolescents who reported risky gambling were more likely to report current and historical engagement in simulated gambling. For example, at-risk adolescents were 6 times more likely to report a history of simu- lated gambling via smartphone apps. The simulated gambling fac- tor with the strongest positive association with pathological gambling score was past exposure to simulated gambling activities (i.e., gambling via Facebook and smartphone apps, and gambling in video-games). In practical terms, over 40% of simulated gamblers reported to have a preoccupation with gambling or an intention to gamble in the future, as compared to 20% for those not involved in simulated gambling. This finding was confirmed by logistic regression analysis, which showed that past involvement in simu- lated gambling activities was a significant predictor of pathological gambling, and was the strongest overall predictor in the model. Every unit increase in the number of past simulated gambling activities doubled the odds of at-risk gambling. Consistent with previous studies of youth simulated gambling (e.g., Byrne, 2004; Griffiths & Wood, 2007), these findings suggest that those youth with a strong interest in gambling are more likely to gamble on these simulated forms. The results were also consistent with re- search that has showed that it is the number of gambling activities (and particular combination of activities) in which a young person engages in that is most strongly predictive of gambling problems (Boldero, Bell, & Moore, 2010).

The findings of this study should be considered in the wider context of gambling in Australia, particularly with respect to the trend towards online and digital gambling (Gainsbury et al., 2011). In 2011–12, the industry generated almost $20 billion in revenue and over 60% of this was attributable to electronic gaming machines (Productivity Commission, 2010). Electronic gambling activities have now expanded to new forms that are readily avail- able and promoted via the internet, mobile applications and in live sports broadcasts. Traditional betting on sports and racing is often now undertaken online and international sites now offer thou- sands of online casino and betting sites that allow people in Aus- tralia to gamble on traditional casino card and table games in realistic simulated environments. Internet gambling is one the

fastest growing areas of gambling in the world and currently the topic of several major Federal Government reviews (Gainsbury et al., 2011; Wood & Williams, 2009).

This study’s findings suggest that the expansion of online and electronic gambling activities is not only within the purview of adult gamblers seeking novel and convenient ways of gambling, but that the growing market has substantial appeal and uptake among those under the legal age of gambling. This study offers only a ‘snap shot’ of this young cohort. It may be speculated that, over time, some young people’s progression of gambling career through previously unavailable, unregulated online gambling activities available 24/7 and complemented by a network of free- to-play gambling simulations, online tutorials, and community for- ums, may lead to the emergence of a new profile of adult gambler. Such a gambler may be distinct in terms of behavioral schedules of betting activity, socio-cultural perceptions of gambling, and pro- pensity for gambling harm.

The popularity of simulated gambling raises the question of whether there exists a need for greater regulatory measures to les- sen or control the level of early exposure of simulated gambling at a population level. One of the main challenges facing regulation is the fact that many of the gambling activities hosted via social med- ia (e.g., Zynga Poker) are operated by third parties based outside of Australia. There is therefore a need for companies that host gam- bling content (e.g., Facebook) to work cooperatively with regula- tors to implement restrictions on who is able to access simulated gambling activities. Another issue is that many simulated gam- bling are positioned or branded as ‘video-games’ on social media sites and online stores (e.g., iStore), and therefore the user may not be initially aware that certain software applications contain gambling features. Young people may also potentially confuse these gambling activities with video-games that primarily involve skill and strategy King, Ejova, & Delfabbro (2012a). Regulators may wish to consider implementation of measures that would require that such content is clearly identified as featuring gambling, and that service providers employ basic social responsibility practices (Friend & Ladd, 2009; Smeaton & Griffiths, 2004).

Another source of exposure is the embedding of gambling material in video-games (Thompson, Tepichin, & Haninger, 2006). In Australia, gambling content in video games is generally classi- fied as all-ages entertainment (King, Delfabbro, et al., 2012). Although the vast majority of these activities are non-financial in nature, there are some ways in which young people are able to convert virtual currency in online multiplayer games (e.g., Runes- cape) into real money. Money is paid to earn credits to gamble and players use these credits to elevate their status relative to other players (Griffiths, King, & Delfabbro, 2009, 2012). In such games, the suppliers avoid the regulatory provisions governing existing gambling activities by not directly providing a monetary return to players, but for all intents and purposes this is a gambling activity which involves the staking of something of value on an activity with an uncertain outcome (i.e., an accepted definition of gambling) (Walker, 1992). Current national estimates suggest that

312 D.L. King et al. / Computers in Human Behavior 31 (2014) 305–313

94% of male adolescents play video games, with mean play-times of 55 min per day (Australian Media Authority, 2007), which sug- gests there is significant potential for exposure to simulated gam- bling opportunities for this demographic group.

4.1. Limitations

This study provides needed data on several emerging types of digital technology-based gambling. Such data are difficult to obtain from industry reports given that: (1) underage access is difficult to track given the lack of barriers for entry and ease of misrepresent- ing age online, and (2) simulated gambling providers may be unli- kely to acknowledge that youth do access these activities, and/or report data on the extent of youth simulated gambling (if known) given the likelihood of negative public perception. The strengths of this study include its large representative sample of adolescents drawn from the general population, and its novelty of measures that assess new types of gambling activity. However, this research had several limitations that warrant mention.

Caution should be employed in interpreting results related to pathological gambling, given the inclusive approach taken to mea- surement of pathological gambling and the relatively low base rate of pathological gambling in the study sample. It should be noted that, although consistent with past research youth gambling, the classification of ‘‘at-risk’’ (i.e., meeting 1 criterion for pathological gambling) employed in this study, should be considered as rela- tively mild in terms of overall clinical risk, and may be lacking in predictive validity. Accounting for this, the results may indicate that simulated gambling has only a subtle influence on gambling cognition (i.e., thoughts about gambling, and intention to gam- bling), and may be less influential in regard to pathological gam- bling per se. It should be noted that this study did not take into account several important individual and familial risk factors (e.g., impulsivity, poor coping skills, and family history of patholog- ical gambling) which are likely to predispose a young person to pathological gambling. Further studies of simulated gambling exposure should include and account for these variables.

Another limitation was the correlational design of the study, which precludes statements of causality. This study does not sug- gest that simulated gambling precedes or follows monetary gam- bling. Prospective longitudinal studies or qualitative studies involving process tracing would be better positioned to obtain such evidence. Self-report error when reporting level of involvement in simulated gambling was another threat to the study’s validity, par- ticularly in relation to obtaining meaningful data of past involve- ment in gambling activities. However, this is a typical drawback of most survey-based studies. Given digital gambling activities may be used frequently and at variable intervals, and portable de- vices enable use within any given social context, further research should consider using objective player tracking data in order to validate self-report measures of gambling (Shaffer, Peller, LaPlante, Nelson, & LaBrie, 2010). Another issue is that current screens for problem gambling refer only to financial gambling, and in some cases there may be a need to assess problematic simulated gam- bling, particularly among young people who lack the financial means to gamble but remain able to engage persistently in free- to-play gambling activities. This may suggest that there is a need for qualitative investigations in order to clarify what adolescents mean by terms such as gambling, and what distinctions, if any, are made between such activities involving money versus virtual credits.

4.2. Conclusions

This study demonstrates that young people’s opportunities for gambling are increasing, largely through the expansion of

technology. These research findings provide evidence that suggests that the pervasive infiltration of gambling technology into young people’s lives is not without its risks. In particular, young people with a history of engagement in simulated gambling activities ap- pear to be at greater risk of behaviors indicative of pathological gam- bling. Even if the present study does not yet support a transition from non-monetary to monetary gambling (i.e., an exposure effect), there is clearly a selection effect which is worthy of attention. The wide availability of simulated and monetary gambling activities has reduced society’s capacity to regulate, monitor, or be aware of the operation of both the suppliers and users of the activities. As gambling activities become increasingly available and embedded within digital and social media technologies, there will remain a need for researchers, clinicians and regulators to be vigilant to the potential risks that new gambling activities pose to youth.

Declaration of interest

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.

Financial disclosure

This study received financial support from a 2012 Young Re- searcher Grant funded by the European Association for the Study of Gambling.

Acknowledgement

With thanks to the students who participated in this study, and the teachers and principals for assistance with data collection.

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