I’ve been working in the area of gambling for nearly 30 years and over the past 15 years I have carrying out research into both online gambling and responsible gambling. As I have outlined in previous blogs, one of the new methods I have been using in my published papers is online behavioural tracking. The chance to carry out innovative research in both areas using a new methodology was highly appealing – especially as I have used so many other methods in my gambling research (including online and offline surveys, experiments in laboratories and ecologically valid settings, offline focus groups, online and offline case study interviews, participant and non-participation observation, secondary analysis of survey data, and analysis of various forms of online data such as those found in online forums and online diary blogs).
Over the last decade there has been a big push by gambling regulators for gambling operators to be more socially responsible towards its clientele and this has led to the use of many different responsible gambling (RG) tools and initiatives such as voluntary self-exclusion schemes (where gamblers can ban themselves from gambling), limit setting (where gamblers can choose how much time and/or money they want to lose while gambling), personalized feedback (where gamblers can get personal feedback and advice based on their actual gambling behaviour) and pop-up messages (where gamblers receive a pop-up message during play that informs them how long they have been playing or how much money that have spent during the session).
However, very little is known about whether these RG tools and initiatives actually work, and most of the research that has been published relies on laboratory methods and self-reports – both of which have problems as reliable methods when it comes to evaluating whether RG tools work. Laboratory experiments typically contain very few participants and are carried out in non-ecologically valid settings, and self-reports are prone to many biases (including social desirability and recall biases). Additionally, the sample sizes are also relatively small (although bigger than experiments).
The datasets to analyse player behaviour are huge and can include hundreds of thousands of online gamblers. Given that my first empirical paper on gambling published in the Journal of Gambling Studies in 1990 was a participant observational analysis of eight slot machine gamblers at one British amusement arcade, it is extraordinary to think that decades later I have access to datasets beyond anything I could have imagined back in the 1980s when I began my research career. The data analysis is carried with my research colleague Michael Auer who has a specific expertise in data mining and we use traditional statistical tests to analyse the data. However, the hardest part is always trying to work out which parameters to use in assessing whether the RG tool worked or not. The kind of data we have includes how much time and money that players are spending on the gambling website, and using that data we can assess to what extent the amount of time and money decreases as a result of using limit setting measures, or receiving personalized feedback or a pop-up message.
One of the biggest problems in doing this type of research in the gambling studies field is getting access to the data in the first place and the associated issue of whether academics should be working with the gambling industry in the first place. The bottom line is that we would never have been able to undertake this kind of innovative research with participant sizes of hundreds of thousands of real gamblers without working in co-operation with the gambling industry. (It should also be noted that the gambling companies in question did not fund the research but provided simply provided access to their databases and customers). In fact, I would go as far as to say the research would have been impossible without gambling industry co-operation. Data access provided by the gambling industry has to be one of the key ways forward if the field is to progress.
Unlike other consumptive and potentially addictive behaviours (smoking cigarettes, drinking alcohol, etc.), researchers can study real-time gambling (and other potentially addictive behaviours like video gaming and social networking) in a way that just cannot be done in other chemical and behavioural addictions (e.g., sex, exercise, work, etc.) because of online and/or card-based technologies (such as loyalty cards and player cards). There is no equivalent of this is the tobacco or alcohol industry, and is one of the reasons why researchers in the gambling field are beginning to liaise and/or collaborate with gambling operators. As researchers, we should always strive to improve our theories and models and it appears strange to neglect this purely objective information simply because it involves working together with the gambling industry. This is especially important given the recent research by Dr. Julia Braverman and colleagues published in the journal Psychological Assessment using data from gamblers on the bwin website showing that self-recollected information does not match with objective behavioural tracking data.
The great thing about online behavioural tracking data collected from gamblers is that it is totally objective (as it provides a true record of what every gambler does click-by-click), is collected from real world gambling websites (so is ecologically valid), and has large sample sizes (typically tens of thousands of online gamblers). There of course some disadvantages, the main ones being that the sample is unrepresentative of all online gamblers (as the data only comes from gamblers at one website) and nothing is known about the person’s gambling activity at other websites (research has shown that online gamblers typically gamble at a number of different websites and not just one). Despite these limitations, the analysis of behavioural tracking data (so-called ‘big data’) is a reliable and cutting-edge way to assess and evaluate online gambling behaviour and to assess whether RG tools actually work in real world gambling settings with real online gamblers in real time.
To get access to such data you have to cultivate a trusting relationship with the data providers. It took me years to build up trust with the gambling industry because researchers who study problem gambling are often perceived by the gambling industry to be ‘anti-gambling’ but in my case this wasn’t true. I am ‘pro-responsible gambling’ and gamble myself so it would be hypocritical to be anti-gambling. My main aim in my gambling research is to protect players and minimise harm. Problem gambling will never be totally eliminated but it can be minimised. If gambling companies share the same aim and philosophy of not wanting to make money from problem gamblers but to make money from non-problem gamblers, then I would be prepared to help and collaborate.
You also need to be thick-skinned. If you are analysing any behavioural tracking data provided by the gambling industry, then you need to be prepared for others in the field criticizing you for working in collaboration with the industry. Although none of this research is funded by the industry, the fact that you are collaborating is enough for some people to accuse you of not being independent and/or being in the pockets of the gambling industry. Neither of these are true but it won’t stop the criticism. Nor will it stop me from carrying on researching in this area using datasets provided by the gambling industry.
Dr. Mark Griffiths, Professor of Gambling Studies, International Gaming Research Unit, Nottingham Trent University, Nottingham, UK
Auer, M. & Griffiths, M.D. (2013). Behavioral tracking tools, regulation and corporate social responsibility in online gambling. Gaming Law Review and Economics, 17, 579-583.
Auer, M. & Griffiths, M.D. (2013). Voluntary limit setting and player choice in most intense online gamblers: An empirical study of gambling behaviour. Journal of Gambling Studies, 29, 647-660.
Auer, M. & Griffiths, M.D. (2014). Personalised feedback in the promotion of responsible gambling: A brief overview. Responsible Gambling Review, 1, 27-36.
Auer, M. & Griffiths, M.D. (2014). An empirical investigation of theoretical loss and gambling intensity. Journal of Gambling Studies, 30, 879-887.
Auer, M. & Griffiths, M.D. (2015). Testing normative and self-appraisal feedback in an online slot-machine pop-up message in a real-world setting. Frontiers in Psychology, 6, 339. doi: 10.3389/fpsyg.2015.00339.
Auer, M. & Griffiths, M.D. (2015). Theoretical loss and gambling intensity (revisited): A response to Braverman et al (2013). Journal of Gambling Studies, 31, 921-931.
Auer, M. & Griffiths, M.D. (2015). The use of personalized behavioral feedback for problematic online gamblers: An empirical study. Frontiers in Psychology, 6, 1406. doi: 10.3389/fpsyg.2015.01406.
Auer, M., Littler, A. & Griffiths, M.D. (2015). Legal aspects of responsible gaming pre-commitment and personal feedback initiatives. Gaming Law Review and Economics, 6, 444-456.
Auer, M., Malischnig, D. & Griffiths, M.D. (2014). Is ‘pop-up’ messaging in online slot machine gambling effective? An empirical research note. Journal of Gambling Issues, 29, 1-10.
Auer, M., Schneeberger, A. & Griffiths, M.D. (2012). Theoretical loss and gambling intensity: A simulation study. Gaming Law Review and Economics, 16, 269-273.
Braverman, J., Tom, M., & Shaffer, H. J. (2014). Accuracy of self-reported versus actual online gambling wins and losses. Psychological Assessment, 26, 865-877.
Griffiths, M.D. (1990). Addiction to fruit machines: A preliminary study among males. Journal of Gambling Studies, 6, 113-126.
Griffiths, M.D. & Auer, M. (2011). Approaches to understanding online versus offline gaming impacts. Casino and Gaming International, 7(3), 45-48.
Griffiths, M.D. & Auer, M. (2015). Research funding in gambling studies: Some further observations. International Gambling Studies, 15, 15-19.
It is often claimed by marketeers that remote gambling makes commercial sense (i.e., the combining of gambling and remote technologies such as the internet and mobile phones into one convenient package). Mobile phone betting and gambling not only provides convenience and flexibility, but perhaps more importantly from a gaming operator’s perspective, provides gambling on the move, whenever and wherever. Since it is somewhat unnatural to always be near a computer, it could be argued that mobile phones are an ideal medium for betting and gambling. Whenever gamblers have a few minutes to spare (at the airport, commuting to work, waiting in a queue, etc.), they can occupy themselves by gambling.
Conventional wisdom says that two things have the power to drive any new consumer technology – pornography and gambling. These activities helped satellite and cable television, video, and the Internet and provide adult entertainment in a convenient and guilt-free environment. Betting via mobile phone is no different. Along with pornography, gambling should have little trouble reaching profitability – especially if this is combined with sports events. Sports interest is huge. There are thousands of communities (including those online). The most successful of those communities will look to ‘mobilize’ and then ‘monetize’.
The mobile phone industry has grown rapidly in the last decade. Market research highlights that mobile phone revenues from mobile gambling and gaming is increasingly rapidly. Although mobile gaming revenues are increasing, it is estimated that less than 2% of mobile industry revenue is generated by gaming and gambling. It is generally thought that lottery gambling will make most money for mobile gambling operators because governments are generally less censorious about lotteries than other forms of gambling. They are also easy to play and relatively low cost compared to other types of gambling.
To some extent, the majority of gamblers are risk-takers to begin with. Therefore, they may be less cautious with new forms of technology. For every day gamblers, mobile phones are ideal for bet placing, and gamblers will be able to check on their bets, and place new ones. Furthermore, it is anonymous, and can provide immediate gratification, anytime, anywhere. Anonymity and secrecy may be potential benefits of mobile gambling as for a lot of people there is still stigma attached to gambling in places like betting shops and casinos. Mobile sports betting is also well suited to personal (i.e., one-to-one) gambling, where users bet against each other rather than bookies. Online betting exchanges demonstrate that people bet on anything and everything to do with sport (with each other).
Although mobile phone technology has improved exponentially over the last decade, it is unlikely that mobile phone graphics and technology will ever truly compete with Internet web browsers (although I am happy to be proved wrong). Intuitively, mobile phone gambling is best suited for sports and event betting. With mobile phone betting, all that is required is real-time access to data about the event to be bet on (e.g., a horse race, a football match), and the ability to make a bet in a timely fashion.
These basic requirements are, of course, easily be provided by the current generation of mobile phones, and the appropriate software. The placing of the bet is not the driving motivation in event wagering. Since being the spectator is what sports fans are really interested in, the sports gambler does not need fulfillment from the process of gambling. People betting on sports will use mobile phones because they are easy, convenient and take no time to boot up. Once they have their sports book registered as a bookmark on their phone, they can access it and place a bet within a very short space of time.
As I have noted in previous blogs, all forms of gambling lie on a chance-skill dimension. Neither games of pure skill nor games of pure chance are particularly attractive to sports bettors. Games of chance (like lotteries) offer no significant edge to sports bettors and are unlikely to be gambled upon. Serious punters gravitate towards types of gambling that provide an appropriate mix of chance and skill. This is one of the reasons why sports betting – and in particular activities like horse race betting – is so popular for gamblers. The edge available in horse race gambling can be sufficient to fully support professional gamblers as they bring their wide range of knowledge to the activity. There is the complex interplay of factors that contributes to the final outcome of the race. However, in the mobile sports betting market, it is likely to be football that will make the big money for sports betting agencies.
Consider the following scenario. A betting service that knows where you are and/or what you are doing has the capacity to suggest something context-related to the mobile user to bet on. For instance, if the mobile phone user bought a ticket for a soccer match using an electronic service, this service may share this information with a betting company. If in that match the referee gives a penalty for one team, a person’s mobile could ring and give the user an opportunity (on screen) to bet whether or not the penalty will be scored. On this type of service, the mobile phone user will only have to decide if they want to bet, and if they do, the amount of money. Two clicks and the bet will be placed. Context, timeliness, simplicity, and above all user involvement look like enough to convince also people that never entered a bet-shop.
Many football clubs are turning themselves into powerful media companies. They have their own digital TV channel and signed up a host of big-name technology partners. Such companies will get the chance to develop co-branded mobile services with the club. This offers users access to content similar to their website (receiving real-time scores and team news via SMS). While watching matches, users will be able to view statistics, player biographies, and order merchandise. Such mobility will facilitate an increase in ‘personalized’ gambling where bettors gamble against each other, rather than the house.
Gambling will (if it is not already) become part of the match day experience. A typical scenario might involve a £10 bet with a friend on a weekend football match. The gambler can text their friend via SMS and log on to the betting service to make their gamble. If the friend accepts, the gambler has got the chance to win (or lose). Football clubs will get a share of the profits from the service. Clubs are keen to get fans using branded mobile devices where they can simply hit a ‘bet’ button and place a wager with the club’s mobile phone partner.
As with all new forms of technological gambling, ease of use is paramount to success. Mobile phones have become more user-friendly. Pricing structures are also important. Internet access and mobile phone use that is paid for by the minute produces very different customer behavior to those that have one off payment fees (e.g., unlimited use and access for a monthly rental fee). The latter payment structure facilitates leisure use, as punters would not be worried that for every extra minute they are online, they are increasing the size of their phone bills. For me, mobile sports betting is where the future of mobile gambling is likely to be.
Dr Mark Griffiths, Professor of Gambling Studies, International Gaming Research Unit, Nottingham Trent University, Nottingham, UK
Griffiths, M.D. (2004). Mobile phone gambling: preparing for take off. World Online Gambling Law Report, 8(3), 6-7.
Griffiths, M.D. (2005). The psychosocial impact of mobile phone gambling. World Online Gambling Law Report, 4 (10), 14-15.
Griffiths, M.D. (2010). The psychology of sports betting: What should affiliates know? i-Gaming Business Affiliate, August/September, 46-47.
Griffiths, M.D. (2011). Mobile sportsbetting: A view from the social sciences. i-Gaming Business, 69, 64-65.
Griffiths, M.D. (2011). Technological trends remote gambling: A psychological perspective. i-Gaming Business, 71, 39-40.
Griffiths, M.D. (2013). Adolescent mobile phone addiction: A cause for concern? Education and Health, 31, 76-78.
Griffiths, M.D. (2007). Mobile phone gambling. In D. Taniar (Ed.), Encyclopedia of Mobile Computing and Commerce (pp.553-556). Pennsylvania: Information Science Reference.
In the summer of 2014 I was commissioned to review problem gambling in Great Britain (the fall out of which I wrote about in detail in a previous blog). Earlier last year, a detailed report by Heather Wardle and her colleagues examined gambling behaviour in England and Scotland by combining the 2012 data from the Health Survey for England (HSE; n=8,291 aged 16 years and over) and the 2012 Scottish Health Survey (SHeS; n=4,815). To be included in the final data analysis, participants had to have completed at least one of the gambling participation questions. This resulted in a total sample of 11,774 participants. So what did the research find? Here is a brief summary of the main results:
- Two-thirds of the sample (65%) had gambled in the past year, with men (68%) gambling more than women (62%). As with the British Gambling Prevalence Survey (BGPS), past year participation was greatly influenced by the playing of the bi-weekly National Lottery (lotto) game. Removal of those individuals that only played the National Lottery meant that 43% had gambled during the past year (46% males and 40% females).
- Gambling was more likely to be carried out by younger people (50% among those aged 16-24 years and 52% among those aged 25-34 years).
- The findings were similar to the previous BGPS reports and showed that the most popular forms of gambling were playing the National Lottery (52%; 56% males and 49% females), scratchcards (19%; 19% males and 20% females), other lottery games (14%; 14% both males and females), horse race betting (10%; 12% males and 8% females), machines in a bookmaker (3%; 5% males and 1% females), slot machines (7%; 10% males and 4% females), online betting with a bookmaker (5%; 8% males and 2% females), offline sports betting (5%; 8% males and 1% females), private betting (5%; 8% males and 2% females), casino table games (3%; 5% males and 1% females), offline dog race betting (3%; 4% males and 2% females), online casino, slots and/or bing (3%; 4% males and 2% females), betting exchanges (1%; males 2% and females 0%), poker in pubs and clubs (1%; 2% males and 0% females), spread betting (1%; 1% males and 0% females).
- The only form of gambling (excluding lottery games) where females were more likely to gamble was playing bingo (5%; 7% females and 3% males).
- Most participants gambled on one or two different activities a year (1.7 mean average across the total sample).
- Problem gambling assessed using the Problem Gambling Severity (PGSI) criteria was reported to be 0.4%, with males (0.7%) being significantly more likely to be problem gamblers than females (0.1%). This equates to approximately 180,200 British adults aged 16 years and over.
- Problem gambling assessed using the criteria of the fourth Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) was reported to be 0.5%, with males (0.8%) being significantly more likely to be problem gamblers than females (0.1%). This equates to approximately 224,100 British adults aged 16 years and over.
- Using the PGSI screen, problem gambling rates were highest among young men aged 16-24 years (1.7%) and lowest among men aged 65-74 years (0.4%). Using the DSM-IV screen, problem gambling rates were highest among young men aged 16-24 years (2.1%) and lowest among men aged over 74 years (0.4%).
- Problem gambling rates were also examined by type of gambling activity. Results showed that among past year gamblers, problem gambling was highest among spread betting (20.9%), played poker in pubs or clubs (13.2%), bet on other events with a bookmaker (12.9%), bet with a betting exchange (10.6%) and played machines in bookmakers (7.2%).
- The activities with the lowest rates of problem gambling were playing the National Lottery (0.9%) and scratchcards (1.7%).
- Problem gambling rates were highest among individuals that had participated in seven or more activities in the past year (8.6%) and lowest among those that had participated in a single activity (0.1%).
The authors also carried out a latent class analysis and identified seven different types of gambler among both males and females. The male groups comprised:
- Cluster A: non-gamblers (33%)
- Cluster B: National Lottery only gamblers (22%)
- Cluster C: National Lottery and scratchcard gamblers only (20%)
- Cluster D: Minimal, no National Lottery [gambling on 1-2 activities] (9%)
- Cluster E: Moderate [gambling on 3-6 activities] (12%)
- Cluster F: Multiple [gambling on 6-10 activities] (3%)
- Cluster G: multiple, high [gambling on at least 11 activities] (1%).
The female groups comprised:
- Cluster A: non-gamblers (40%)
- Cluster B: National Lottery only gamblers (21%)
- Cluster C: National Lottery and scratchcard gamblers only (7%)
- Cluster D: Minimal, no National Lottery (8%)
- Cluster E: moderate, less varied [2-3 gambling activities, mainly lottery-related] (8%)
- Cluster F: moderate, more varied [2-3 gambling activities but wider range of activities] (6%)
- Cluster G: multiple [gambling on at least four activities] (6%)
Using these groupings, the prevalence of male problem gambling was highest among those in Cluster G: multiple high group (25.0%) followed by Cluster F: multiple group (3.3%) and Cluster E: moderate group (2.6%). The prevalence of problem gambling was lowest among those in the Cluster B; National Lottery Draw only group (0.1%) followed by Cluster C: minimal – lotteries and scratchcards group (0.7%). The prevalence of female problem gambling was highest among those in the Cluster G: multiple group (1.8%) followed by those in Cluster F: moderate – more varied group (0.6%). The number of female gamblers was too low to carry out any further analysis. The report also examined problem gambling (either DSM-IV or PGSI) by gambling activity type.
- The prevalence of problem gambling was highest among spread-bettors (20.9%), poker players in pubs or clubs (13.2%), bettors on events other than sports or horse/dog races (12.9%), betting exchange users (10.6%) and those that played machines in bookmakers (7.2%).
- The lowest problem gambling prevalence rates were among those that played the National Lottery (0.9%) and scratchcards (1.7%).
- These figures are very similar to those found in the 2010 BGPS study although problem gambling among those that played machines in bookmakers was lower (7.2%) than in the 2010 BGPS study (8.8%).
- As with the BGPS 2010 study, the prevalence of problem gambling was highest among those who had participated in seven or more activities in the past year (8.6%) and lowest among those who had taken part in just one activity (0.1%). Furthermore, problem gamblers participated in an average 6.6 activities in the past year.
Given that the same instruments were used to assess problem gambling, the results of the most recent surveys using data combined from the Health Survey for England (HSE) and Scottish Health Survey (SHeS) compared with the most recent British Gambling Prevalence Survey (BGPS) do seem to suggest that problem gambling in Great Britain has decreased over the last few years (from 0.9% to 0.5%). However, Seabury and Wardle again urged caution and noted:
“Comparisons of the combined HSE/SHeS data with the BGPS estimates should be made with caution. While the methods and questions used in each survey were the same, the survey vehicle was not. HSE and SHeS are general population health surveys, whereas the BGPS series was specifically designed to understand gambling behaviour and attitudes to gambling in greater detail. It is widely acknowledged that different survey vehicles can generate different estimates using the same measures because they can appeal to different types of people, with varying patterns of behaviour…Overall, problem gambling rates in Britain appear to be relatively stable, though we caution readers against viewing the combined health survey results as a continuation of the BGPS time series”.
There are other important caveats to take into account including the differences between the two screen tools used in the BGPS, HSE and SHeS studies. Although highly correlated, evidence from all the British surveys suggests that the PGSI and DSM-IV screens capture slightly different groups of problem gamblers. For instance, a 2010 study that I co-authored with Jim Orford, Heather Wardle, and others (in the journal International Gambling Studies) using data from the 2007 BGPS showed that the PGSI may under-estimate certain forms of gambling-related harm (particularly by women) that are more likely to be picked up by some of the DSM-IV items. Our analysis also suggested that the DSM-IV appears to measure two different factors (i.e., gambling-related harm and gambling dependence) rather than a single one. Another important distinction is that the two screens were developed for very different purposes (even though they are attempting to assess the same construct). The PGSI was specifically developed for use in population surveys whereas the DSM-IV was developed with clinical populations in mind. Given these differences, it is therefore unsurprising that national surveys that utilize the screens end up with slightly different results comprising slightly different groups of people.
It also needs stressing (as noted by the authors of most of the national gambling surveys in Great Britain) that the absolute number of problem gamblers identified in any of the surveys published to date has equated to approximately 60 people. To detect any significant differences statistically between any of the studies carried out to date requires very large sample sizes. Given the very low numbers of problem gamblers and the tiny number of pathological gamblers, it is hard to assess with complete accuracy whether there have been any significant changes in problem and pathological gambling between all the published studies over time. Wardle and her colleagues concluded that:
“Overall, based on this evidence, it appears that problem gambling rates in England and Scotland are broadly stable. Whilst problem gambling rates according to either the DSM-IV or the PGSI were higher in 2010, the estimate between 2007 and the health surveys data were similar. Likewise, problem gambling rates according to the DSM-IV and the PGSI individually did not vary statistically between surveys, meaning that they were relatively similar” (p.130).
Dr. Mark Griffiths, Professor of Gambling Studies, International Gaming Research Unit, Nottingham Trent University, Nottingham, UK
Griffiths, M.D. (2014). Problem gambling in Great Britain: A brief review. London: Association of British Bookmakers.
Orford, J., Wardle, H., Griffiths, M.D., Sproston, K. & Erens, B. (2010). PGSI and DSM-IV in the 2007 British Gambling Prevalence Survey: Reliability, item response, factor structure and inter-scale agreement. International Gambling Studies, 10, 31-44.
Seabury, C. & Wardle, H. (2014). Gambling behaviour in England and Scotland. Birmingham: Gambling Commission.
Wardle, H. (2013). Gambling Behaviour. In Rutherford, L., Hinchliffe S., Sharp, C. (Eds.), The Scottish Health Survey: Vol 1: Main report. Edinburgh.
Wardle, H., Moody. A., Spence, S., Orford, J., Volberg, R., Jotangia, D., Griffiths, M.D., Hussey, D. & Dobbie, F. (2011). British Gambling Prevalence Survey 2010. London: The Stationery Office.
Wardle, H., & Seabury, C. (2013). Gambling Behaviour. In Craig, R., Mindell, J. (Eds.) Health Survey for England 2012 [Vol 1]. Health, social care and lifestyles. Leeds: Health and Social Care Information Centre.
Wardle, H., Seabury, C., Ahmed, H., Payne, C., Byron, C., Corbett, J. & Sutton, R. (2014). Gambling behaviour in England and Scotland: Findings from the Health Survey for England 2012 and Scottish Health Survey 2012. London: NatCen.
Wardle, H., Sproston, K., Orford, J., Erens, B., Griffiths, M. D., Constantine, R., & Pigott, S. (2007). The British Gambling Prevalence Survey 2007. London: National Centre for Social Research.
Wardle, H., Sutton, R., Philo, D., Hussey, D. & Nass, L. (2013). Examining Machine Gambling in the British Gambling Prevalence Survey. Report by NatCen to the Gambling Commission, Birmingham.