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Gainsbury et al. (in press) How risky is Internet gambling?

How risky is Internet gambling? A comparison of subgroups of Internet gamblers based on

problem gambling status

Sally M. Gainsburyᵃᵇ, Alex Russellᵃᵇ, Robert Woodᶜ, Nerilee Hingᵃ, Alex Blaszczynskiᵃᵇ

Gainsbury, S., Russell, A., Wood, R., Hing, N., & Blaszczynski, A. (in press). How risky is Internet

gambling? A comparison of subgroups of Internet gamblers based on problem gambling status. New

Media & Society. Published OnlineFirst Jan 15, 2014. doi:10.1177/1461444813518185

ᵃ Centre for Gambling Education & Research, Southern Cross University

ᵇ School of Psychology, University of Sydney

ᶜ University of Lethbridge

Please address all correspondence to:

Dr. Sally Gainsbury

Centre for Gambling Education & Research, Southern Cross University

P.O. Box 157, Lismore NSW 2480, Australia

Email: sally.gainsbury@scu.edu.au

Declaration of Conflicting Interests: The authors have no conflicts of interest to declare in

relation to this manuscript.

Funding acknowledgement: This work was supported by the Menzies Foundation [Allied

Health Grant to the first author].

Abstract Internet gambling offers unique features that may facilitate the development or exacerbation of

gambling disorders. Higher rates of disordered gambling have been found amongst Internet as

compared to land-based gamblers; however little research has explored whether Internet

disordered gamblers are a distinct subgroup. The current study compared problem with non-

problem and at-risk Internet gamblers to further understand why some Internet gamblers

experience gambling-related harms. A sample of 2,799 Australian Internet gamblers completed

an online survey. Problem gambling respondents were younger, less educated, had higher

household debt, lost more money and gambled on a greater number of activities, and were more

likely to use drugs while gambling as compared to non-problem and at-risk gamblers. Problem

gamblers had more irrational beliefs about gambling, were more likely to believe the harms of

gambling to outweigh the benefits, that gambling is morally wrong, and that all types of

gambling should be illegal. For problem gamblers, Internet gambling poses unique problems

related to electronic payment and constant availability leading to disrupted sleeping and eating

patterns. However, a significant proportion of Internet problem gambling respondents also had

problems related to terrestrial gambling, highlighting the importance of considering overall

gambling involvement when examining subgroups of gamblers. Policy makers should carefully

consider how features of Internet gambling contribute to gambling disorders requiring the

implementation of evidence-based responsible gambling strategies.

Keywords: Internet gambling, problem gambling, disordered gambling, risk factors, Australia,

addiction, irrational beliefs, demographic characteristics

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Gainsbury et al. (in press) How risky is Internet gambling?

Introduction Internet gambling has changed the nature of gambling through sophisticated electronic

technology offering convenient and constant global access to novel and interactive types of

gambling. Internet gambling, synonymous with online, remote or interactive gambling, refers to

all forms of wagering and gambling accessible through computer, mobile phone or wireless

Internet connected devices. The global Internet gambling market was estimated to be worth

US$28.32 billion in 2012 and forecasted to rise to US$49.64 billion by 2017 (GBGC, 2013).

Regulated markets with local licenses represent an increasing revenue share with 40% accounted

for by local licensed operators in 2012; an increase from 38% in 2011 (GBGC, 2013). This

reflects an increasing trend for international jurisdictions to regulate the provision of Internet

gambling, including consumer protection and harm minimisation strategies, in addition to

economic incentives such as taxation (Gainsbury and Wood, 2011).

The importance of implementing strong consumer protection policies is based on findings from

several large-scale surveys indicating that Internet gamblers are more likely to be at risk of

developing gambling disorders and harms compared to gamblers only using land-based forms

(Gainsbury, Wood, Russell, Hing, and Blaszczynski 2012; Gainsbury, Russell, Hing, Wood, &

Blaszczynski, in press; Griffiths, Wardle, Orford et al., 2009; Wood and Williams, 2007, 2010).

Several features unique to Internet gambling potentially make this mode of access more

problematic for players. Internet gambling is highly accessible and convenient, and allows

continuous uninterrupted periods of playing multiple games with rapidly determined outcomes.

Used in private settings, predominately at home (Gainsbury et al., 2012; Wood and Williams,

2011), Internet gambling is easier to conceal from others. The immersive nature of the Internet

may also contribute to dissociative states, in which players may lose track of time and money

spent, facilitating excessive gambling (Corney and Davis, 2010; Griffiths, 2003; Griffiths and

Parke, 2002; Monaghan, 2009). Electronic payment systems may have a lower psychological

value than cash (Corney and Davis, 2010; Griffiths, 2003) by ‘tokenizing’ money, and by

reducing the time taken to deposit funds, redeem payouts, or receive loyalty benefits; described

by Schull (2012) in reference to electronic gaming machines, as improved cash acquisition

modalities. As suggested by Schull (2012), large scale complex tracking and data mining of

consumer behaviour and expenditure triangulated to demographic data allow operators to

effectively offer customized products targeting distinct player profiles.

Several studies have compared non-Internet or terrestrial gamblers with Internet gamblers,

including investigation of rates of problem gambling (Gainsbury et al., 2012; Gainsbury et al., in

press a, c; Wardle Moody, Griffiths et al., 2011; Wood and Williams, 2011; Woolley, 2003). It

has been argued that several elements of Internet gambling may lead to more problem gambling,

such as the ability to play in isolation, the immersive nature of the Internet, the ability to wager

large sums, and through the use of electronic funds and credit (Monaghan, 2009; Griffiths and

Parke, 2002; Siemens and Kopp, 2011). However, there are relatively few studies examining

subgroups of Internet gambling based on problem gambling severity, which are important to

understand why some Internet gamblers may experience problems, while others do not. Given

the potential for Internet gambling to lead to problems, this paper aims to examine differences

and similarities between sub-groups of Internet gamblers at-risk and with and without gambling

problems. The objective is to acquire a greater understanding of the specific features of Internet

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Gainsbury et al. (in press) How risky is Internet gambling?

gambling that may lead to harm and individual factors that may make players vulnerable to

developing problems.

Comparison of Internet and terrestrial gamblers

Several studies have found that Internet gamblers are typified by individuals from higher socio-

economic strata, work full-time, have higher levels of education, and are more technologically

savvy in comparison to land-based gamblers (Gainsbury et al., 2012; Gainsbury et al., in press b;

Griffiths, et al., 2009; Wardle Moody, Griffiths et al., 2011; Wood and Williams, 2011; Woolley,

2003). Some research suggests that Internet compared to terrestrial gamblers are more likely to

use alcohol and drugs (Griffiths et al., 2009; Wood and Williams, 2011), and to be relatively

more involved by participating in multiple gambling forms and modes (Author 2012; Griffiths et

al., 2009; Wardle et al., 2011; Wood and Williams, 2011). However, the characteristics of

Internet gambling users appear to be changing over time. For example, a Swedish longitudinal

study found that more recent adopters of Internet gambling were younger and had lower levels of

education than those who started gambling online prior to 2008, and that the gender gap among

Internet gamblers had decreased over time (Svensson and Romild, 2011). Newer Internet

gamblers had a history of gambling on land-based forms, suggesting that gamblers migrated to

online play, rather than adopting it in the first instance. These results suggest that Internet

gamblers should not be considered as a homogeneous or static subpopulation.

Risk factors for disordered Internet gambling

Several analyses of actual online gambling expenditure indicate that the majority of Internet

gamblers spend relatively moderate amounts, although a small proportion have high levels of

expenditure and exhibit other risky patterns of play (Gainsbury et al., 2012; LaBrie et al, 2008;

LaPlante et al., 2009; Nelson et al., 2008). The 2010 British Gambling Prevalence Survey

revealed those who used the Internet for multiple types of gambling were more likely to be

categorised as problem gamblers compared to Internet gamblers who engaged in fewer Internet

gambling activities (Wardle et al., 2011). This is consistent with previous findings that versatility

(or the number of gambling activities engaged in) and frequency of play are important predictors

of gambling problems (Gray et al., 2012; Holtgraves, 2009; LaPlante et al., 2009). Moreover, the

strength of the relationship between Internet and gambling problems is substantially diminished

when controlling for such factors as frequency and versatility of gambling (Halme, 2011;

LaPlante et al., 2009; Philander & MacKay, 2013; Vaughan Williams et al., 2008; Welte et al.,

2004; Welte et al., 2009). A study of Australian problem gamblers found that compared to

terrestrial gamblers, Internet gamblers were younger and engaged in a greater number of

gambling activities, and these differences were significantly greater for problem than moderate-

risk gamblers (Gainsbury et al., in press c). Taken together, these results indicate that Internet

problem gamblers, as a group, are characterized by important distinctions from their non-

problem gambling counterparts.

The current study

As participation and expenditure for online gambling increase (Gainsbury and Wood, 2011;

Wardle et al., 2011) it is important to understand risk factors associated with this activity.

Theoretical models of gambling disorders have largely been developed based on previous

research, which generally has not specifically considered Internet gamblers (Siemens and Kopp,

2011). Similarly, the majority of existing prevention, harm minimisation and treatment programs

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Gainsbury et al. (in press) How risky is Internet gambling?

do not cater purposefully for gamblers who have problems with, or are at risk of problems

related to Internet gambling. This research attempts to determine how problem Internet gamblers

differ from non-problem Internet gamblers and what identifiable risk factors may predict Internet

gambling problems. The objective is to advance the conceptual understanding of problem

gambling and guide harm reduction policies and treatment programs.

Methods

Procedure

Two Australian universities human research ethics committees approved the research study.

Recruitment advertisements inviting participants to complete an online survey were placed on a

variety of websites between December 2010 and August 2011. These included websites relating

to land-based gambling venues, Australian Internet wagering sites, and sites hosted by sporting

organisations, government offices and help-providers. Paid advertisements were also placed on

Google and Facebook. The majority of respondents indicated that they completed the survey

based on advertisements from participating websites (72.9%) with only a minority recruited from

Facebook (10.0%), Google (4.2%), or other via other sources. Individuals who clicked on these

advertisements were directed to the survey home page which outlined the inclusion criteria,

informed consent, the purpose of the survey, and voluntary nature of participation. This online

survey was adapted from an instrument previously used by Wood and Williams (2010); however,

independent researchers have not been validated the measure’s psychometric properties.

The survey contained several sections:

1. Gambling Behavior Scale. This scale measured the frequency of participation over the

past 12 months (seven options ranging from ‘4 or more times a week’ to ‘not at all in the

past 12 months’) in ten different gambling (for money) activities, including: instant win

scratch tickets, lottery tickets, keno, wagering on sporting events, wagering on dog or

horse races, bingo, games of skill, poker (against individuals), electronic gaming

machines (EGMs), and casino table games. Those who had participated at least once in

each activity provided further information about their average monthly gambling

expenditure (defined as ‘how much you are ahead or behind, or your net win (+) or loss (-

) in an average MONTH in the past 12 months’) as well as the proportion of each

gambling activity they conducted online. Participants were also asked about their alcohol

and drug consumption while gambling, with five response options ranging from ‘never’

to ‘always’.

2. Internet Gambling Questions. Participants completed 13 questions, including the primary

location where they gamble online (home, work, or in transit), the year they first gambled

online, the time of day they typically gamble online, and their preferred medium for

online gambling. Participants were asked to nominate, from a designated list, the factors

that motivated them to choose particular Internet gambling sites over others, and the

advantages and disadvantages of Internet gambling as compared to land-based play.

Participants were asked if their gambling had ever caused significant problems, if it had

impacted their sleep or eating patterns, and whether the use of electronic money (e.g.,

credit cards or Internet bank transfers) had impacted their gambling expenditure.

3. Gambling Attitudes. Three questions assessed attitudes to gambling, specifically:

perceived benefit or harm of gambling, whether gambling is morally wrong and whether

gambling should be legalised.

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Gainsbury et al. (in press) How risky is Internet gambling?

4. Gambling Knowledge and Beliefs Test. Ten multiple choice items were designed to

assess aspects of commonly held gambling fallacies, including independence of randomly

determined events, personal luck, winning and losing streaks, probabilities of winning,

and outcomes of prolonged gambling sessions. For example, questions included whether

the probability of winning was different based on the numbers picked for a lottery draw

or whether an EGM had recently paid out. Participants received a score of +1 for each

correct response, for a total score of 0 to 10 (high scores reflecting greater resistance to

gambling fallacies).

5. Problem Gambling Severity Index (Ferris & Wynne, 2001). Problem gambling status was

measured using the nine-item Problem Gambling Severity Index (PGSI) of the Canadian

Problem Gambling Index (CPGI). Nine questions assessed the extent of gambling-related

harm experienced over the previous 12 months, with response options of ‘never’,

‘sometimes’, ‘most of the time’, and ‘almost always’. The scoring procedure and

classification of participants followed the instruction described by Ferris and Wynne

(2001); total score of zero indicated a no-risk gambler, 1-2 = a low risk gambler, 3-7 =

moderate-risk and 7+ a possible problem gambler. The PGSI has been independently

validated, and results indicate that it has excellent reliability, dimensionality,

external/criterion validation, item variability, practicality, applicability, and

comparability (McMillen and Wenzel, 2006; Neal, Delfabbro, and O’Neill, 2004).

6. Demographics. Standard questions assessed gender, age, marital status, education,

employment, household income, and household debt.

Analyses

Internet gamblers were defined as those who indicated that they had gambled online at least once

in the past 12 months. Although somewhat broad, this definition was considered appropriate for

this study given that even infrequent Internet gambling may have an impact on the development

of harm. Furthermore, this definition is consistent with previous studies (Griffiths et al., 2009;

Ladd and Petry, 2002; Olason et al., 2011; Wood and Williams, 2011), and is similar to the

definition of gambling used in most prevalence studies, thereby enabling inter-study comparison

of results.

Independent samples t-tests were employed for continuous dependent variables. Chi-square tests

were used for all other dependent variables, including examining standardized residuals for

dependent variables with more than two levels, using a critical standardized residual of 2.

A logistic regression was conducted to determine the characteristics that differentiate Internet

problem gamblers from Internet non-problem gamblers. A total of 14 predictor variables were

used: gender, state of residence, employment status, marital status, household income, alcohol

use, illicit drug use, level of education, household debt, age, gambling expenditure, number of

different gambling behaviours, gambling attitudes and gambling knowledge/beliefs. All

categorical variables were dummy coded using the following reference groups: gender (female),

state (NSW), employment status (full-time employment), marital status (married) and education

level (less than high school).

All continuous variables were checked for skewness, corrected with the following

transformations: PGSI score (log transformation), household debt (square root transformation)

and number of gambling behaviours (square root transformation). The skewness of gambling

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Gainsbury et al. (in press) How risky is Internet gambling?

expenditure, gambling attitude and gambling knowledge/beliefs could not be corrected.

Gambling expenditure was particularly leptokurtic, which was reduced by winsoring the extreme

1% of values (114 values in total) and the final result for all values was divided by 1,000 so that

expenditure was measured in thousands of dollars. There were no issues with the variables

measuring age or household income.

Due to the large sample size, an alpha of 0.001 was used and effect sizes are reported for all t-

tests and chi-square analyses. For t-tests, Cohen’s d is reported and, using Cohen’s guidelines

(Cohen, 1992), 0.2 indicates a small effect, 0.5 a medium effect and 0.8 a large effect. For chi-

square, the (phi) coefficient was used, where values between -0.3 and 0.3 may be treated as

trivial associations. However, all results that are statistically significant at alpha = 0.001 with a

phi coefficient between -0.3 and 0.3 are reported, but should be interpreted with caution. Because

a conservative alpha level was used for all analyses, no further type I error controls were used.

All analyses were conducted using SPSS v18.0.3 on an Apple Intel MacBook Pro.

Results

Participants

Of the 4,680 Internet gamblers that started the survey, 2,799 provided responses to the PGSI,

giving a completion rate of 59.8%. Of these, 2,344 (83.7%) were identified as being below the

threshold for possible problem gambling (retaining the original nomenclature for the scale),

which included non-problem gamblers (27.8%), low-risk gamblers (27.1%), and moderate-risk

gamblers (28.9%). For the analyses presented in this paper, this group will be referred to as non-

problem and at-risk gamblers (NP/ARGs) The remaining 455 were identified as possible

problem gamblers (16.3%), referred to as problem gamblers (PGs) in the remainder of the

current paper.

Demographics

PGs were, on average, younger (M = 39.2, SD = 13.1) than NP/ARGs (M = 47.4, SD = 14.4),

t(684.4) = 12.05, p < 0.001, d = 0.60 (Table 1). PGs and NP/ARGs also differed in terms of

relationship status, 2

(4, N=2,787) = 73.86, p < 0.001, = 0.1, education level, 2

(8, N=2,791) =

28.55, p < 0.001, = 0.10, and employment status, 2

(6, N=2,791) = 71.0, p < 0.001, = 0.16.

From an inspection of standardised residuals, PGs are more likely to have never married, have

less formal education, and be unemployed or a student in comparison to NP/ARGs.

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Gainsbury et al. (in press) How risky is Internet gambling?

Table 1. Demographic profile of non-problem and at-risk gamblers and problem gamblers

Non-problem &

at-risk gamblers

(N=2,344)

Problem

gamblers

(N=455)

Gender

Male 93.0% 93.2%

Female 7.0% 6.8%

ns

Age bracket

Under 18 0.3% 0.0%

18-19 0.8% 1.8%*

20-29 13.3% 26.2%*

30-39 16.4% 25.9%*

40-49 23.0% 23.5%

50-59 23.8%* 15.2%

60-69 17.4%* 6.6%

70-79 4.6%* 0.7%

80 or older 0.7% 0.2%

2 (8, N = 2,799) = 122.61, p < 0.001, = 0.21

Marital status

Married 52.0%* 31.7%

Living with partner 16.5% 21.4%*

Widowed 1.6% 1.3%

Divorced or separated 9.1% 10.1%

Never married 20.7% 35.5%*

2 (4, N = 2,787) = 73.86, p < 0.001, = 0.16

Current employment status

Employed full-time 60.1% 65.8%*

Employed part-time 8.5% 10.6%

Unemployed and seeking work 1.6% 5.6%*

Retired 16.9%* 5.8%

Homemaker 1.1% 1.1%

Full-time student 3.2% 5.6%*

Sick leave, maternity leave, on strike, on disability OR other 8.6%* 5.4%

2 (6, N = 2,729) = 71.03, p < 0.001, = 0.16

Highest completed education level

No school 0.0% 0.4%

Some primary school 0.0% 0.2%

Completed primary school 0.7% 1.3%

Some high school 11.0% 16.5%*

Completed high school 23.3% 25.3%

Some technical school, college or university 11.2% 12.8%

Completed technical school/TAFE/diploma trade certification 23.8%* 19.4%

Completed undergraduate university degree 18.9%* 15.0%

Professional degree (law, medicine, dentistry), MSc, PhD 11.1% 9.0%

2 (8, N = 2,791) = 28.55, p < 0.001, = 0.10

Note: Asterices indicate significant differences between the groups.

Gambling behaviour

The difference between the average monthly expenditure over all forms of gambling was not

statistically significantly different between problem gambling groups due to the large amount of

variance in the obtained estimates. However, when the extreme 1% of values were trimmed from

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Gainsbury et al. (in press) How risky is Internet gambling?

the data, NP/ARGs were found to report being ahead by an average of AUD$437.76 per month

as compared to a reported average monthly loss of AUD$895.80 for PGs, t(601.12) = 5.96, p <

0.001, d = 0.67.

PGs engaged in a significantly lower proportion of sports betting online via computer (64.9%),

compared to NP/ARGs (77.3%), t(377.36) = 6.40, p < 0.001, d = 0.43 (Table 2). The same was

found for Internet race wagering, where a significantly lower proportion of betting for PGs

(59.5%) occurred online, compared to 75.1% for NP/ARGs, t(420.25) = 8.21, p < 0.001, d =

0.51. Correspondingly, PGs conducted a significantly higher proportion of their race wagering in

terrestrial agencies (38.0%), compared to 29.6% for NP/ARGs, t(403.82) = 4.35, p < 0.001, d =

0.29. No other statistically significant differences were found relating to gambling activities.

Table 2 – Mean (and SD) of reported percentage of betting via each medium for non-problem

and at-risk gamblers and problem gamblers

Non-problem & at-

risk gamblers

(N=2,344)

Problem gamblers

(N=455)

Sports betting

Land-based agencies 26.1% (28.4%) 33.2% (29.1%)

Internet via computer 77.3% (27.2%) 64.9% (29.8%)

Internet via mobile or wireless device 20.5% (27.3%) 21.8% (27.0%)

Telephone 10.0% (16.7%) 10.4% (11.9%)

Interactive TV 0.4% (1.6%) 1.3% (5.4%)

Horse and dog racing

Land-based agencies 29.6% (28.9%) 38.0% (29.3%)

Internet via computer 75.1% (29.0%) 59.5% (31.8%)

Internet via mobile or wireless device 21.6% (29.9%) 24.2% (29.5%)

Telephone 11.0% (18.3%) 13.8% (17.2%)

Interactive TV 2.0% (10.7%) 2.1% (7.8%)

The proportion of PGs who used alcohol while gambling (78.4%) was not significantly different

to NP/ARGs (74.3%). However, 15.5% of PGs reported at least some drug use while gambling,

compared to 8.6% for NP/ARGs, 2 (1, N=2,742) = 20.43, p < 0.001, = 0.09.

Impact of Internet gambling on gambling problems

Of all participants, 19% indicated that their gambling had caused them significant problems. This

included 74.2% of the subset classified as PGs by the PGSI, in addition to a proportion of

participants whose PGSI scores fell below this threshold but who self-reported at least some

gambling-related problems.

The majority of those reporting gambling problems (61.5%) stated that problems occurred after

first gambling online. The use of electronic funds was reported to increase the amount spent by a

greater proportion of PGs (53.5%), compared to NP/ARGs (11.8%), 2

(2, N=2,720) = 463.53, p

< 0.001, = 0.41. PGs were also more likely to gamble online between midnight and 6am

(3.3%) than NP/ARGs (1.5%), although the majority of both groups (50.9% vs. 61.0%) still

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Gainsbury et al. (in press) How risky is Internet gambling?

gambled between 12pm and 6pm. The disruptive effects of online gambling were reflected in the

finding that 47.9% of PGs reported sleep patterns being affected, with only 8.6% of NP/ARGs

reporting similar disturbances (1, N=2,743) = 456.98, p < 0.001, = 0.40. Similarly, 33.5% of

PGs reported disruptions to eating patterns, compared to only 3.9% of NP/ARGs, 2

(1, N=2,720)

= 416.75, p < 0.001, = 0.39.

With respect to the selection of Internet gambling sites, significantly more PGs (18.7%) reported

that they were influenced by incentives provided by online gambling sites, compared to

NP/ARGs (11.8%), 2

(1, N=2,799) = 16.18, p < 0.001, = 0.08, although the effect was small.

When asked about the advantages of Internet gambling compared to gambling at land-based

facilities, PGs were more likely to report a preference for 24 hour availability/convenience

(66.4% vs. 50.6%), 2

(1, N=2,799) = 37.89, p < 0.001, = 0.12; greater privacy/anonymity

(41.3% vs. 32.1%), 2

(1, N=2,799) = 14.43, p < 0.001, = 0.07; and better game experiences

(20.9% vs. 12.8%), 2

(1, N=2,799) = 20.53, p < 0.001, = 0.09, as compared NP/ARGs. In

terms of disadvantages, PGs were more likely to say that online gambling was too convenient

(57.8% vs. 28.6%), 2

(1, N=2,799) = 145.88, p < 0.001, = 0.23; more addictive (38.0% vs.

11.8%), 2

(1, N=2,799) = 193.92, p < 0.001, = 0.26; and easier to spend more money (56.0%

vs. 27.0%), 2

(1, N=2,799) = 148.87, p < 0.001, = 0.23.

Gambling knowledge and attitudes

PGs were significantly more likely to believe that the harm of gambling far outweighs the

benefits and significantly less likely to say the benefits are about equal to the harms or somewhat

outweigh the harm compared to NP/ARGs. A significantly higher proportion of PGs reported the

belief that gambling is morally wrong (or were unsure) compared to NP/ARGs. Finally, a

significantly higher proportion of PGs believed that all types of gambling should be illegal, while

a significantly lower proportion of PGs believed that all types of gambling should be legal (see

Table 3). Regarding the knowledge and beliefs test, PGs, on average, had significantly lower

scores (M = 7.13, SD = 1.83) than NP/ARGs (M = 7.60, SD = 1.67), t(2791) = 5.34, p < 0.001, d

= 0.26.

Table 3 – Percentage of responses to questions about gambling attitudes for non-problem/at-risk

gamblers and problem gamblers

Non-problem &

at-risk gamblers

(N=2,332)

Problem

gamblers

(N=454)

Belief about the benefit or harm that gambling has for society

The harm far outweighs the benefits 18.3% 52.0%*

The harm somewhat outweighs the benefits 26.3% 26.3%

The benefits are about equal to the harm 34.6%* 14.8%

The benefits somewhat outweigh the harm 12.5%* 4.0%

The benefits far outweigh the harm 8.4%* 2.9%

2 (4, N = 2,764) = 268.26, p < 0.001, = 0.31

Do you believe that gambling is morally wrong?

Yes 2.3% 15.0%*

No 94.9%* 75.1%

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Gainsbury et al. (in press) How risky is Internet gambling?

Unsure/don’t know 2.9% 9.9%*

2 (2, N = 2,785) = 204.92, p < 0.001, = 0.27

Which of the following best describes your opinion about legalized gambling?

All types of gambling should be illegal 1.3% 8.1%*

Some types of gambling should be legal and

some should be illegal OR don’t

know/unsure

59.3% 62.1%

All types of gambling should be legal 39.4%* 29.7%

2 (2, N = 2,786) = 84.25, p < 0.001, = 0.17

Note: Asterices indicate significant differences between the groups.

Characteristics statistically differentiating problem gamblers from non-problem and at-

risk gamblers

A total of 2,493 respondents had completed all relevant questions and were thus included in the

logistic regression. Of these, 2,079 were NP/ARGs and 414 were PGs. The lowest tolerance

measured for any variable was 0.451 for one of the education dummy variables, suggesting no

problems with multicollinearity.

The test of the overall model, with 14 predictors, was statistically significant, 2

(34, N=2,493) =

541.17, p < 0.001 (Nagelkerke pseudo-R2 = 0.329) indicating that, altogether, these predictors

reliably differentiate problem Internet gamblers from non-problem Internet gamblers. Overall

prediction success was found to be 85.6%. The percentage of PGs correctly predicted by the

model was only 26.8%, but for NP/ARGs the correctly predicted percentage was 97.4%. Table 4

outlines the predictor variables, including regression coefficients, Wald statistics, significance

and odds ratio for each of the predictors, including dummy variables.

Controlling for all other variables in the model, the significant predictors differentiating PGs

from NP/ARGs were: those who had completed a technical school, college or diploma

(compared to those who have not finished school), higher household debt, lower age, losing

more money through gambling, higher number of gambling activities, were more likely to

believe that gambling is more harmful than beneficial and more likely to believe that gambling is

morally wrong.

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Gainsbury et al. (in press) How risky is Internet gambling?

Table 4 – Logistic regression of characteristics differentiating problem gamblers from non-

problem & at-risk gamblers

Predictor b S.E. (b) Wald Significance Odds

Ratio

Gender -0.276 0.254 1.179 0.278 0.759

State (ref NSW)

ACT 0.253 0.569 0.198 0.656 1.288

Victoria -0.438 0.202 4.701 0.030 0.645

Queensland 0.079 0.171 0.215 0.643 1.083

South Australia 0.269 0.237 1.287 0.257 1.309

Western Australia -0.067 0.407 0.027 0.869 0.935

Tasmania 0.195 0.522 0.140 0.708 1.215

Northern Territory 0.248 0.552 0.201 0.654 1.281

Employment Status (ref employed full time)

Employed part-time 0.056 0.223 0.064 0.800 1.058

Unemployed and seeking work -0.554 0.339 2.664 0.103 0.575

Retired 0.311 0.291 1.145 0.285 1.365

Homemaker -0.267 0.554 0.233 0.629 0.766

Full-time student 0.483 0.312 2.406 0.121 1.621

Sick leave, other 0.304 0.269 1.283 0.257 1.355

Marital Status (ref married)

Living with partner -0.398 0.181 4.829 0.028 0.671

Widowed -0.252 0.544 0.215 0.643 0.777

Divorced or separated -0.383 0.239 2.566 0.109 0.682

Never married -0.377 0.194 3.784 0.052 0.686

Household income -0.071 0.024 8.960 0.003 0.931

Alcohol Use 0.049 0.161 0.094 0.759 1.051

Drug Use -0.176 0.182 0.935 0.334 0.839

Education level

Completed high school 0.526 0.204 6.638 0.010 1.691

Some technical school, college or

university

0.594 0.246 5.808 0.016 1.811

Completed technical school,

college or diploma

0.781 0.211 13.722 <0.001 2.183

Completed undergraduate

university degree

0.697 0.230 9.150 0.002 2.008

Professional degree 0.635 0.265 5.766 0.016 1.888

Household debt 0.139 0.038 13.367 <0.001 1.149

Age -0.030 0.007 20.207 <0.001 0.971

Expenditure ($,000’s) -0.104 0.020 26.257 <0.001 0.902

Number of gambling behaviours 1.054 0.177 35.445 <0.001 2.868

Perceived benefits of gambling -0.689 0.066 110.027 <0.001 0.502

Perceived morality of gambling -0.759 0.117 41.805 <0.001 0.468

Perceptions about legalisation of

gambling

-0.177 0.124 2.030 0.154 0.838

Gambling knowledge/beliefs -0.092 0.037 6.171 0.013 0.912

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Gainsbury et al. (in press) How risky is Internet gambling?

Discussion

This study has found that amongst Internet gamblers, PGs are distinct from NP/ARGs in a

number of important ways. PGs are more likely to be younger, less educated, and have greater

debts, as compared to NP/ARGs. This is generally consistent with the characteristics of problem

gamblers which have been identified in other studies, both in Australia and internationally

(Productivity Commission, 2010; Reith, 2006; Shaffer and Korn, 2002). Although younger age

was a predictor of problem gambling amongst Internet gamblers, the average age of this group

was 39. This indicated that Internet gambling problems are not restricted to, or necessarily more

common amongst young adults, who are often considered to be at greater risk of developing

gambling problems generally (Productivity Commission, 2010; Reith, 2006; Welte, Barnes,

Tidwell et al., 2011) and for Internet gambling specifically (McBride and Derevensky, 2009;

Olason et al., 2011; Petry and Weinstock, 2007). However, it should be noted that the current

survey was not open to those age less than 18 years so conclusions cannot be drawn about this

group. These results indicate that populations vulnerable to developing gambling problems may

use Internet gambling, and that the accessibility of this medium may lead to, or exacerbate,

gambling problems. More research is needed, including longitudinal studies, to ascertain whether

the gambling problems reported by Internet gamblers reflect a shift in the media used to gamble

amongst individuals with gambling problems or the emergence of a new subset of problem

gamblers, or a combination of these explanations.

Not surprisingly, as negative consequences and gambling disorders are typically correlated with

financial losses, PGs appear to be losing significantly greater amounts of money than NP/ARGs.

Approximately half of the PGs reported that the use of electronic payment methods increased

their expenditure. This may result from the lower awareness of expenditure when transferring

money between accounts, as opposed to using cash (Corney and Davis, 2010; Griffiths, 2003).

Notwithstanding, this group also engaged in a greater number of gambling activities, which

likely contributed to their gambling losses. These findings confirm previous studies that suggest

gambling disorders may be predicted by examining overall gambling involvement (Holtgraves,

2009; LaPlante et al., 2009; Welte et al., 2009). Furthermore, PGs had poorer scores on the

gambling knowledge and beliefs test due to endorsing a greater number of gambling fallacies.

This may indicate that PGs may misunderstand how outcomes are determined and have irrational

beliefs about gambling, which may lead to risky betting patterns. Irrational beliefs are commonly

identified as an important component of problem gambling, driving continued play despite the

occurrence of negative consequences (Blaszczynski and Nower, 2002). These findings are

supported by previous research that found Internet gambling was associated with increased

irrational thinking and gambling frequency, making this form of gambling particularly risky

(Lund, 2011).

Certain features of Internet gambling may increase the risk of developing problems, namely

those considered to be preferred by PGs as opposed to NP/ARGs such as ease of access, privacy

and anonymity and better game experience. These features enable prolonged sessions of

immersive play without disturbance, and are likely to be associated with reported disturbances to

sleeping and eating patterns. Such disruptions to lifestyle have been associated with other types

13

Gainsbury et al. (in press) How risky is Internet gambling?

of excessive Internet use (Cao and Su, 2006; Guan and Subrahmanyam, 2009), indicating that

the constant availability of Internet gambling may intensify the negative consequences of

gambling. Poor physical health and disrupted sleeping and eating patterns are also associated

with other types of gambling problems (Morasco, Pietrzak, Blanco et al., 2006), although the

ease and convenience of Internet gambling may render this particularly disruptive. The attraction

of incentives may also encourage PGs to act on urges and gamble more than they intended. This

is likely facilitated by the convenience of Internet gambling and the proliferation of Internet

advertising, included targeted pop-up and email offers (Monaghan, 2009), which may be more

easily ignored by NP/ARGs.

Previous studies have found Internet gamblers are more likely to use illicit drugs than non-

Internet gamblers (Gainsbury et al, in press b; Wood and Williams, 2011). However, these

results suggest that drug use may be more common amongst Internet PGs, which is consistent

with a large body of evidence demonstrating high rates of comorbidity between substance use

and gambling disorders (Kessler et al., 2008; Petry, Stinson and Grant, 2005) and may be

facilitated by the privacy of Internet gambling. Anecdotal reports have suggested that stimulants

are used to extend sessions of online gambling (Gainsbury, 2012); however, further research is

required to investigate substance use amongst Internet gamblers.

Of interest, Internet PGs reported some awareness of the risky nature of Internet gambling and

were significantly more likely to state that Internet gambling was ‘too convenient’, ‘more

addictive’ and made it ‘easier to spend money’ as compared to land-based gambling. The

tendency for PGs to place a greater proportion of their bets at land-based venues as compared to

NP/ARGs may reflect a preference for terrestrial venues, familiarity with this mode of betting, or

an attempt to reduce expenditure. However, both groups reported that the majority of their bets

are placed online. This latter finding coupled with the finding that convenience and ease of

access are commonly cited as advantages of Internet gambling, may reflect a broader betting

behavioural shift towards online wagering sites and applications (Gainsbury et al., 2012; Wood

and Williams, 2011). The concurrent findings that the ease of access for Internet gambling is

both an advantage and disadvantage poses problems for operators and regulators from a public

health perspective as this feature poses benefits for the majority of players, but simultaneously

can lead to problems for a proportion of gamblers.

PGs had more negative attitudes towards gambling, which expands the previous research of

Wood and Williams (2011), which found that more positive attitudes towards gambling was a

significant predictor of someone being an Internet gambler. This finding is interesting given

their tendency to be involved in a wide variety of gambling activities, and perhaps indicates their

awareness of their lack of control over the extent of their gambling and the related negative

consequences or the gambling losses they have experienced. The results are somewhat consistent

with previous research showing that problem gamblers anticipate both the positive and negative

outcomes of gambling and may suggest that disordered gamblers continue to gamble due to their

positive feelings personally for the activity, but they can appreciate the negative consequences of

gambling overall for society (Gillespie et al., 2007). These findings further highlight the

importance of considering sub-groups of Internet gamblers when making policy decisions.

14

Gainsbury et al. (in press) How risky is Internet gambling?

Although the majority of PGs stated that their gambling problems developed after they first

gambled online, nearly two-fifths had existing problems before gambling online. Furthermore, as

gambling problems develop over time, subsequent gambling problems may be related to other

modes of gambling. The causal direction of gambling problems cannot be determined from this

cross-sectional study. However, Gainsbury et al. (in press c) explored the mechanisms for

Internet gambling contributing to the onset of gambling problems and found that Internet

problem gamblers appear to systematically differ from terrestrial problem gamblers. To further

investigate the role of Internet gambling in causing or exacerbating gambling problems future

research should include longitudinal studies, which would enable causal pathways to be

examined in more detail than is possible in a cross-sectional study.

Limitations

Due to the self-selected nature of the online survey, the results should not be considered to be

representative of the entire population of Australian Internet gamblers. Few women were

included in the study precluding gender comparisons. Furthermore, a substantial proportion of

participants did not complete the PGSI, meaning that the results may have some bias that should

be considered. In efforts to keep the survey from being too lengthy, some variables of interest

were not fully explored. Therefore, ongoing research is needed to explore the use of Internet

gambling, particularly by adolescents, who were not included in the current study. Some aspects

of Internet gambling were difficult to measure in a self-report survey format. In particular, results

for questions on expenditure had great variability in responses, making accurate interpretation of

results problematic. Research indicates that individuals perceive and report gambling

expenditure in different ways, and that retrospective estimates of gambling expenditures may be

unreliable (Wood and Williams, 2007). Self-report of the negative consequences of gambling is

also based on participant’s subjective considerations, which may vary between individuals. The

definition of Internet gamblers is also very inclusive and future research concentrating on a more

involved sample of Internet gamblers would add to this field of inquiry. Research is already

underway to overcome some of these limitations.

Conclusions

As Internet gambling becomes more popular worldwide, and as participation increases, it is

likely that the number of Internet gambling disorders will continue to increase (Gainsbury and

Wood, 2011). However, not all Internet gamblers are likely to develop problems, so it is

important to identify characteristics that may indicate greater risk for some individuals. The

demographic predictors of gambling disorder amongst Internet gamblers are similar to those that

predict problem gambling in other populations. It is possible that the increased availability and

accessibility of Internet gambling is creating a shift towards this mode of access amongst

problem gamblers who may have traditionally used other modes of access. This study shows that

although the majority of Internet gamblers engage in this activity without experiencing

significant harmful consequences, certain features of Internet gambling appears to pose specific

risks. Specifically, the use of electronic money transfers, the constant accessibility, and the

anonymity and privacy of play may facilitate excessive expenditure, particularly amongst

vulnerable individuals.

15

Gainsbury et al. (in press) How risky is Internet gambling?

An increasing number of jurisdictions are regulating Internet gambling and implementing

responsible gambling tools and prevention strategies to minimise the risks of harm posed to

players and vulnerable groups (Gainsbury and Wood, 2011). The increased legalisation of

Internet gambling accompanied by strict responsible gambling and consumer protection

regulations will likely increase player’s access to gambling sites that have stronger consumer

protection measures. Existing treatment and prevention strategies have been developed based on

terrestrial gamblers, however, these may not be applicable to Internet gamblers (Simens and

Kopp, 2011). Future research is needed to evaluate the effectiveness of online responsible

gambling and prevention strategies, with the goal of increasing the effectiveness of such

interventions. Research should also consider any potential unintended consequences of increased

consumer protection measures, such as a portion of players moving to less regulated sites to

avoid any restrictions on gambling. Internet gambling requires gamblers to be identified and play

with registered accounts, which may enable responsible gambling strategies and tools to be

customised based on individual patterns of play. Further research examining the behavioural

differences between problem and non-problem players will enable the identification of

behavioural markers that can be used to trigger interventions (LaBrie and Shaffer, 2011).

This is one of the first studies to specifically examine predictors of problematic Internet

gambling amongst a large sample of Internet gamblers, and as such makes an important

contribution to the research field and to inform policies. As problem Internet gamblers appear to

have irrational thoughts and erroneous beliefs about how outcomes are determined, players

should be directed to accurate information about how outcomes are determined upon opening an

account and at periodic intervals. Internet gambling sites should be mandated to display clearly

communicated information about games more prominently for players and enable limit setting to

minimise excessive spending.

16

Gainsbury et al. (in press) How risky is Internet gambling?

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Author Biographies

Dr. Sally Gainsbury has led several Australian studies examining interactive gambling and

evaluating responsible gambling strategies, including dynamic messaging. She has been invited

to provide expert advice to the government organisations and committees in Australia, Canada

and the UK on the topic of responsible gambling, harm minimization and Internet gambling and

her research has been cited in relation to numerous responsible gambling proposals. She is the

Editor of International Gambling Studies.

Alex Russell is an early career researcher involved in various research fields, including

gambling, olfactory perception and synaesthesia. He is studying his PhD at the University of

Sydney looking at wine perception and has been employed as a statistical consultant for

numerous research projects. He is currently employed at Southern Cross University as Chief

Statistical Analyst on numerous gambling projects with the Centre for Gambling Education and

Research.

Dr. Robert Wood is a Professor in the Department of Sociology, and Dean of the School of

Graduate Studies at the University of Lethbridge. He currently serves as the Vice-President of

the Western Canadian Deans of Graduate Studies association, the Secretary-Treasurer of the

Canadian Association for Graduate Studies, and on the Board of Directors for the Alberta

Gambling Research Institute. Dr. Wood's research program spans the areas of youth culture,

theories of deviance, and the socio-cultural aspects of problem gambling. His scholarly work has

been widely published in numerous fora, including books, journal articles, and educational

programs.

Professor Nerilee Hing (PhD) is Founding Director of the Centre for Gambling Education and

Research, Southern Cross University. Her research interests include, problem gambling,

responsible gambling, gambling amongst vulnerable populations, Internet gambling, and help-

seeking for gambling problems.

Dr. Blaszczynski is Professor and Director of the Gambling Treatment Clinic and Research Unit

at the University of Sydney. He was chairman of the Working Party for the Australian

Psychological Society and is currently the editor-in-chief of International Gambling Studies.

Dr. Blaszczynski received the American Council of Problem Gambling Directors Award in 1995,

the National Centre for Responsible Gambling Senior Investigator’s Research Award in 2004,

and most recently, the New South Wales Government’s Responsible Gambling Fund’s

Excellence Award for contributions to gambling in 2013.