Griffiths, M.D. & Barnes, A. (2008). Internet gambling: An online empirical study among student gamblers. International Journal of Mental Health and Addiction, 6, 194-204.

Int J Ment Health Addiction (2008) 6:194–204 DOI 10.1007/s11469-007-9083-7 Internet Gambling: An Online Empirical Study Among Student Gamblers Mark Griffiths & Andrew Barnes Received: 6 November 2006 / Accepted: 12 April 2007 / Published online: 15 May 2007 # Springer Science + Business Media, LLC 2007 Abstract It has been noted that the introduction of the Internet to gambling activities may change some of the fundamental situational and structural characteristics and make them potentially more addictive and/or problematic. This study examined some of the differences between Internet gamblers and non-Internet gamblers. Based on past literature it was hypothesised that (1) males would be significantly more likely to be Internet gamblers than females, (2) Internet gamblers would be significantly more likely to be problem gamblers than non-Internet gamblers, and (3) males would be significantly more likely to be problem Internet gamblers than females. A self-selected sample of 473 student respondents (213 males; 260 females) aged between 18 and 52 years (mean age =22 years; SD=5.7 years) participated in an online survey. All three hypotheses were confirmed. The results suggest the structural and situational characteristics of Internet gambling may be having a negative psychosocial impact on Internet gambling. This is most notably because of increased number of gambling opportunities, convenience, 24-h access and flexibility, increased event frequencies, smaller intervals between gambles, instant reinforcements, and the ability to forget gambling losses by gambling again immediately. It is suggested that further research needs to be carried out into the effects that the Internet has in facilitating gambling behaviour. Keywords Gambling . Internet gambling . Problem gambling . Online gambling Modern day gambling is a very profitable business with many different and varied new ways to take part in gambling activities such as gambling via the Internet, mobile phone and interactive television (Griffiths 2003a). The rise in Internet gambling activity has been very rapid. However, to date, there has been little empirical research carried out. All over the world, there has been a major shift by governments towards deregulation of the M. Griffiths (*) : A. Barnes International Gaming Research Unit, Psychology Division, Nottingham Trent University, Burton Street, Nottingham NG1 4BU, UK e-mail: mark.griffiths@ntu.ac.uk Int J Ment Health Addiction (2008) 6:194–204 195 gambling industry which has taken gambling out of traditional gambling environments and has led to increases in access and opportunity to gamble remotely (Griffiths 2006). Although there have been few empirical studies carried out on the psychosocial effects of Internet gambling, there have been a number of theoretical papers written on the potential changes the Internet may make to the gambling activity (e.g., Griffiths 1996; 1999; 2003a; Griffiths and Parke 2002; Griffiths and Wood 2000; Parke and Griffiths 2004; Griffiths et al. 2006). It has been noted that the introduction of the Internet to gambling activities may change some of the fundamental situational and structural characteristics and make them potentially more addictive and/or problematic (Griffiths 2003a). One of the main changes that the Internet brings to gambling is that gambling activities are brought into the home and workplace environment. This potentially means that Internet gambling can become an inhouse or work activity. Other major situational changes of Internet gambling include accessibility and convenience. These two situational characteristic changes mean that Internet gambling is easily accessible to anyone with an Internet connection and an electronic payment method, 24-h a day, 7 days a week. This is in contrast to casino gambling and bookmakers where travelling and membership rules may be deterrents to excessive and/or continuous gambling. There are also other concerns such as the use of electronic cash facilitating the suspension of judgement and the rapid event frequencies of many Internet games. Similarly, the Internet provides the gambler with a sense of anonymity. This can change the psychological effects that gambling has. Griffiths (2003a) suggested the anonymity the Internet provides allows gamblers the chance to gamble without the fear of stigma, and if heavy losses occur, nobody will see the face of the loser. Another key factor in Internet gambling is associability (Griffiths 2003a). In many cases, the Internet makes the activity more asocial (although some online gambling activities like online poker have chat room facilities allowing some social interaction). Asociable gambling removes a psychological and social “safety net” from gamblers as there are no friends or acquaintances to help monitor their gambling. There have been a small number of empirical studies to date including those in the UK (Griffiths 2001), Canada (Ialomiteanu and Adalf 2001) and the US (Ladd and Petry 2002) although all of these are somewhat old given the speed at which the Internet gambling field has been moving, or have concentrated on just one particular type of gambling such as online poker (e.g., Wood et al. 2007). Furthermore, all of them have methodological weaknesses that makes generalisation to national populations suspect. There have been recent press reports in the UK (and elsewhere) that large numbers of university students may be experiencing financial problems as a direct result of Internet gambling (Wood et al. 2007). The UK Consumer Credit Counselling Service, a UK charity specialising in debt counselling, claimed that an increasing number of British students were experiencing financial problems as a consequence of their Internet gambling. Reasons for participation have been speculative, but include the wide availability to students who all have familiarity with using the Internet, greater flexibility in their working schedules, and the increased freedom experienced when leaving home. It would appear that students may be a vulnerable population when it comes to Internet gambling. Internet gamblers appear to be a difficult sub-population to examine although Wood and Griffiths (2007) have suggested that collecting online data from online populations might be highly advantageous. For instance, online surveys may be an appropriate method for getting large cost effective samples for particular online sub-populations (like online gamblers). 196 Int J Ment Health Addiction (2008) 6:194–204 The following study attempts to examine some of the differences between Internet gamblers and non-Internet gamblers in a student sub-sample. Based on past literature it is hypothesised that (1) males will be significantly more likely to be Internet gamblers than females, (2) Internet gamblers will be significantly more likely to be problem gamblers than non-Internet gamblers, and (3) males will be significantly more likely to be problem Internet gamblers than females. Materials and Methods Participants A sample of 473 student respondents (213 males; 260 females) aged between 18 and 52 years (mean age=22 years; SD=5.7 years) participated. Respondents were contacted initially by an e-mail request sent to students of a UK East Midlands university on a variety of undergraduate courses. Design Following a small successful pilot study, data were collected via an online questionnaire asking participants about their gambling and Internet gambling behaviour. The study was particularly interested in influences concerning the gender of the gambler (males versus females), type of gambler (i.e., Internet gambler versus non-Internet gambler), and problem gambling (problem gambler versus non-problem gambler). Materials The questionnaire was Internet-based and was constructed using an “in house” software package for creating online questionnaires (i.e., Autoform). The questionnaire was divided into three parts and examined general gambling behaviour (Section A), Internet gambling behaviour (Section B), and demographic information about the participant (Section C). More specifically, Section A examined gambling and problem gambling behaviour. Questions were asked relating to how often participants gambled, how much time and money were spent on gambling activities, and the types of gambling they engaged in. Problem gambling was assessed using the South Oaks Gambling Screen (SOGS; Lesieur and Blume 1987). Each participant was given a SOGS score. Those scoring 5 or above on the SOGS were defined as problem gamblers for the purposes of this study. Section B of the questionnaire was very similar to section A and covered all the same questions except the focus was specifically on Internet gambling. For the purposes of this study, an “Internet gambler” was defined as anybody who had ever gambled on the Internet. However, this section did include some specific additions. Questions were asked relating to how participants paid for Internet gambling (e.g., credit card, debit card, e-wallets, etc.) and overall trustworthiness on the Internet. They were also asked relating to factors involved in the initial decision to gamble (e.g., the role of family, friends, advertising, demo games, anonymity, ease of access, 24-h gambling, etc.). Section C asked questions relating to a number of demographic factors (including age, gender, ethnicity, etc.) of each participant. Int J Ment Health Addiction (2008) 6:194–204 197 Procedure Approximately 2,000 students across a variety of undergraduate courses were sent an e-mail asking them to participate in a study of student gambling behaviour. Each of the e-mails included a hyperlink to the online survey. Participants were asked to fill in all appropriate sections and questions of the questionnaire as accurately as possible. When the participant finished the survey and was happy with the information given, a ‘submit’ button was pressed. Participants were given the researchers’ e-mail addresses and were told that if they had any questions concerning the survey they could e-mail the research team. All data collected were confidential and anonymous. The Autoform software automatically collated all the participants’ data into SPSS format ready for analysis. Results Gender Differences in Gambling, Internet Gambling, and Problem Gambling Gambling Participation Of the 473 participants, 371 of them (78.4%) had gambled comprising 178 males (84% of the total males) and 193 females (74% of the total females). In relation to Internet gambling, 105 (22% of the total sample) had gambled on the Internet comprising 89 males (85% of all Internet gamblers) and 16 females (15% of all Internet gamblers) (X2 =77.5, p=0.001). A total of 26 participants were defined as problem gamblers according to SOGS scores (5.5% of the total sample). Of these 26 participants, 21 were males (81% of problem gamblers) and five were female (19% of problem gamblers), and 20 had gambled on the Internet (77% of problem gamblers) and six had not (23% of problem gamblers). Male gamblers (50%) were significantly more likely to have gambled on the Internet compared to female gamblers (8%) (X2 =79.4, p=0.001). Problem gamblers were significantly more likely to be male (80.8%) than female (19.2%) (X2 =12.0, p=0.001). Problem gamblers were also significantly more likely to have gambled on the Internet (77%) than not (23%) (X2 =32.6, p=0.001). Gambling Frequency and Preferred Types of Gambling by Gender Gambling Frequency and Spend Over half of males (51%) gambled more than once a month compared to only a fifth of females (20%). One-way ANOVAs showed that males gambled significantly more often than females (F=72.7, d.f. [1,370], p=0.0001). Just over three-quarters of females spent £1 or less on gambling per week (76.3%) compared to twothirds of males (65.7). Approximately 9% of males spent more than £50 a week gambling (see Fig. 1). One way ANOVAs showed males also gambled spent significantly more money in a week than females (F=90, d.f. [1, 370], p=0.0001). Types of Gambling Activity Playing the lottery was the most popular form of gambling with over four-fifths of males (82%) and females (90%) participating (see Table 1). There were major gender differences in most types of gambling activity. Significantly more males than females gambled on horse races, on sporting event, at the casino, and private betting with friends (see Table 1). Females were significantly more likely to play bingo (see Table 1). 198 Int J Ment Health Addiction (2008) 6:194–204 Male Female 80 70 % of subjects 60 50 40 30 20 10 0 £1 or less £1 $1.01-£5 £5.01-£10 £10.01£20 £20.01£50 £50 or more Amount of money gambled in a week Fig. 1 Weekly gambling spend by males and females Internet Gambling Frequency and Preferred Types of Gambling Gambling Frequency and Spend Over 60% of Internet gamblers gambled more than once a week compared to less than 20% of non-Internet gamblers (see Fig. 2). One-way ANOVAs showed that Internet gamblers were significantly more likely to gamble more often (F= 103.9, d.f. = [1,370], p=0.001). Over half of Internet gamblers (60.4%) spent more than five pounds a week gambling. Over two-thirds of non-Internet gamblers (71.8%) spent less than £1 a week (see Fig. 3). One-way ANOVAs showed that Internet gamblers spent Table 1 Gender Differences in Gambling by Gambling Activity Type of Gambling % Males % Females Chi Squared Significance Lottery Scratchcards Horse race betting Dog race betting Sports betting Casino gambling Slot machines Private bets with friends Bingo 82 62.4 52.2 36 68 53.4 61.8 70.8 15.2 90 66.3 37.3 24 18.7 25.9 51.3 38.3 42 5.17 0.63 8.37 5.95 92.3 29.4 4.2 39.2 32.2 0.23 0.426 0.004** 0.015* 0.001** 0.001** 0.042* 0.001** 0.001** ** Significant at 1% level; * Significant at 5% level (all d.f.=1) Int J Ment Health Addiction (2008) 6:194–204 199 Internet gambler Non-Internet gambler 40 35 30 % of subjects 25 20 15 10 5 0 Every day A few days a week Once a week A few times a month Once a month A few times a year Annually Less than once a year How often subjects gamble Fig. 2 Internet gambler versus non-Internet gambler gambling participation significantly more money on gambling in a week (F=139.9 d.f. = [1,370], p=0.001) compared to non-Internet gamblers. Types of Gambling Activity Over half of Internet gamblers and non-Internet gamblers had gambled on lottery games, scratchcards and fruit machines (see Table 2). Significantly more Internet gamblers gambled on horse races, sporting events, in a casino and making private bets with their friends (see Table 2). Problem Gambling Frequency and Preferred Types of Gambling Gambling Frequency and Spend One-way ANOVAs showed that problem gamblers gambled significantly more often than non-problem gamblers (F=45.9 d.f [1, 370], p=0.001) and spent significantly more money a week gambling (F=87.1, d.f. [1, 370], p=0.001). 200 Int J Ment Health Addiction (2008) 6:194–204 Internet gambler Non-Internet gambler 70 60 %of subjects 50 40 30 20 10 0 £1 or less £1 $1.01-£5 £5.01£10 £10.01£20 £20.01£50 £50 or more Amount of money gambled in a week Fig. 3 Weekly gambling spend by Internet gamblers versus non-Internet gamblers Types of Gambling Activity Problem gamblers gambled on many activities with over twothirds of problem gamblers gambling on the Lottery, horse and dog racing, sports betting casino games, fruit machines and gambling with friends (see Table 3). The most gambled on activity by problem gamblers was casino games (88.5%). Problem gamblers were significantly more likely to gamble on slot machines, on horse races, on dog races, in a casino, and make private bets with friends (see Table 3). Table 2 Internet Gamblers Versus Non-Internet Gamblers on Gambling Activities Type % Internet gamblers % Non-Internet gamblers Chi Squared Significance Lottery Scratchcards Horse race betting Dog race betting Sports betting Casino gambling Slot machines Private bets with friends Bingo 86.7 66.7 60 38.1 75.2 65.7 55.2 67.6 21.0 86.1 63.1 38.3 26.7 29.3 28.6 56.8 48.5 31.3 0.02 0.32 14.3 4.7 65.0 43.6 0.07 11.1 4.7 0.86 0.57 0.001** 0.03* 0.001** 0.001** 0.79 0.001** 0.03* ** Significant at 1% level; * Significant at 5% level (all d.f.=1) Int J Ment Health Addiction (2008) 6:194–204 201 Table 3 Problem Gamblers Versus Non-Problem Gamblers on Gambling Activities Types of Gambling % Problem Gamblers % Non-problem Gamblers Chi squared Significance Lottery Scratchcards Horse race betting Dog race betting Sports betting Casino gambling Fruit machines Private bet with friends Bingo 76.9 69.2 69.2 61.5 73.1 88.5 80.8 80.8 34.6 87 64.1 49.6 27.5 40 35.4 54.5 51.9 28.7 2.1 0.3 6.9 13.3 10.8 28.6 6.8 8.1 0.4 0.15 0.60 0.008** 0.001** 0.001** 0.001** 0.009** 0.004** 0.52 **Significant at 1% level (all d.f.=1) Internet Gambling Gambling Participation As mentioned above, 105 participants (89 males and 16 females) gambled on the Internet. The most popular forms of Internet gambling were online sports betting (68%), online poker (48%), online casino gambling (47%), horse race betting (36%), Internet lotteries (32%), online scratchcards (15%), and online slot machines (14%). The most popular form of online payment was with debit cards (92.5% of Internet gamblers had used their debit cards to gamble). Influences on Internet Gambling Internet gamblers reported many factors that influenced them in the decision to gamble online. The main reasons were ease of access (84%), flexibility of use (75%), 24-h availability (66%), because friends do (67%), large gambling choice (57%), advertising (40%), anonymity (25%), demo games (21%) and because family members do (14%). Four-fifths of Internet gamblers considered the Internet a trustworthy medium of gambling (79%). Most Internet gamblers preferred to gamble with online operators who also had offline gambling facilities (e.g., high street bookmakers) (90%). The majority of Internet gamblers considered Internet gambling easier to conceal than nonInternet gambling (84.9%) with nearly a third of Internet gamblers (32%) hiding their gambling from family members. Discussion The results confirmed all three hypotheses. Internet gamblers were significantly more likely to be problem gamblers. Furthermore, males were significantly more likely to be Internet gamblers and Internet problem gamblers. Results also showed that Internet gamblers spent significantly more time and money gambling than non-Internet gamblers. The results could perhaps be interpreted in two ways. Firstly, it may be because problem gamblers are more frequent gamblers that they gamble in a wider range of different media including the Internet. Alternatively, it might be that the Internet makes the gambling activity more problematic for the individual. Internet games have increased event frequencies that in turn lead to instant reinforcements and the ability to forget about losses. This may contribute to 202 Int J Ment Health Addiction (2008) 6:194–204 the increased incidence of Internet problem gambling. Changes to the situational and structural characteristics of Internet gambling (outlined in the introduction) are likely to be contributing factors in any increased levels of problem gambling among those using the Internet. Internet gamblers rated flexibility, ease of access, and 24-h availability as very beneficial to Internet gambling. However, these benefits may lead to sustained periods of gambling that in some people may lead to gambling problems. When gambling on the Internet, there are few protective controls, which means that gambling can be accessed when gamblers are (say) intoxicated. This may lead to rash gambling and larger losses, which in turn could facilitate chasing behaviour—a major feature of problem gambling (Lesieur 1994). Another reasonable explanation of the findings could be that problem gamblers are simply using the Internet as a convenient medium to gamble on an activity they are already experiencing problems with. However, this is still cause for concern as it suggests that the Internet is providing a facilitating factor in the development of already vulnerable individuals. Internet gamblers in this study were more likely to participate in fast action, high arousing games such as casino gambling and sports betting. These types of gambling are known to be more problematic than activities like lottery playing. The results also suggest the structural and situational characteristics of Internet gambling may be having a negative psychosocial impact on Internet gambling. This is most notably because of increased number of gambling opportunities, convenience, 24-h access and flexibility, increased event frequencies, smaller intervals between gambles, instant reinforcements, and the ability to forget gambling losses by gambling again immediately. The data clearly showed that the majority of problem gamblers were male and had gambled on the Internet. This supports literature showing that problem gambling is more likely to be a male problem (e.g., Sproston et al. 2000). Very few Internet gamblers in this study were female. This is noteworthy as there has been much press coverage in the UK with online gaming companies claiming that females are outnumbering males on many of their Internet gambling sites. This study does not back up such anecdotal claims. The suggestion that Internet gambling may be contributing to higher rates of problem gambling is of major concern. Furthermore, the Internet is difficult to regulate and police, and many legal and geographical boundaries are transcended. There is also the issue of gambling operators having to be more socially responsible in remote gambling environments. For instance, Smeaton and Griffiths (2004) carried out an exploratory study examining the social responsibility features of Internet gambling sites. They found that among the sites there were great variations with some sites having little or no age verification checks and most of the sites offering no reference or referral to gambling help organisations. The study also found that many Internet gambling operators carried out very poor (if any) age verification checks. Often it was simply the ticking of a “Yes, I am over the age of 18 years,” leaving minors free to gamble on the Internet with the misuse of credit cards or accessing accounts of people they know. This is of concern given the consistent finding that earlier introduction to gambling is likely to lead to greater problems (see overviews by Griffiths 2002; 2003b; Hayer et al. 2005). Although the Internet creates regulatory problems, Smeaton and Griffiths’ findings coupled with the data from this study suggest there is a need for better Internet gambling legislation. Griffiths has suggested many simple guidelines for Internet gamblers to follow that should help to minimise problem gambling (Griffiths 2003a). These guidelines include: the implementation of reliable age verification checks; setting credit limits and the ability to self-exclude from the site; having built in pauses on the site; and references to helping organisations. The last of these guidelines could be especially useful if the gambler is Int J Ment Health Addiction (2008) 6:194–204 203 directed to online help sites. As some authors have suggested (Griffiths and Cooper 2003; Griffiths 2005), online therapy for gambling problems is of potentially more benefit than offline help as the non face-to-face communication may help to minimise the social stigma that having a gambling problem often causes. There are, of course, a number of limitations to the study reported here. Firstly, the sample was self-selecting and may not be representative of either gamblers or Internet gamblers. It is uncertain whether Internet gamblers would be more or less likely to fill out an online questionnaire. Given the lack of data in the area of Internet gambling, this study has one of the biggest samples so far in the field and as such the data are of existential value. Secondly, the data were self-report. However, there is evidence that data collected via computer-mediated communication is often a more truthful medium of communication than face-to-face conversations (Walther 1996; Wood and Griffiths 2007). Thirdly, the sample consisted of students only and is again not representative of the general public. However, it has been argued that students are a vulnerable sub-group (Wood et al 2007) and therefore data relating to this particular sub-set of gamblers is again of existential value. Finally, it is likely this study had a relatively low response rate (about 25%) given the number of e-mails that were originally sent out. This again raises questions of how representative of students the overall sample is. Furthermore, we do not know what differentiates those that responded to this survey from those that did not participate. Clearly, a study like this should be replicated using a random sample of the general population with a more robust response rate. It is apparent that the medium of the Internet does seem to have an affect on both situational and structural characteristics of many gambling activities. 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