A diction for addiction: A brief overview of our papers at the 2017 International Conference on Behavioral Addictions

This week I attended (and gave one of the keynote papers at) the fourth International Conference on Behavioral Addictions in Haifa (Israel). It was a great conference and I was accompanied by five of my colleagues from Nottingham Trent University all of who were also giving papers. All of the conference abstracts have just been published in the latest issue of the Journal of Behavioral Addictions (reprinted below in today’s blog) and if you would like copies of the presentations then do get in touch with me.

Griffiths, M.D. (2017). Behavioural tracking in gambling: Implications for responsible gambling, player protection, and harm minimization. Journal of Behavioral Addictions, 6 (Supplement 1), 2.

• Social responsibility, responsible gambling, player protection, and harm minimization in gambling have become major issues for both researchers in the gambling studies field and the gaming industry. This has been coupled with the rise of behavioural tracking technologies that allow companies to track every behavioural decision and action made by gamblers on online gambling sites, slot machines, and/or any type of gambling that utilizes player cards. This paper has a number of distinct but related aims including: (i) a brief overview of behavioural tracking technologies accompanied by a critique of both advantages and disadvantages of such technologies for both the gaming industry and researchers; (ii) results from a series of studies carried out using behavioural tracking (particularly in relation to data concerning the use of social responsibility initiatives such as limit setting, pop-up messaging, and behavioural feedback); and (c) a brief overview of the behavioural tracking tool mentor that provides detailed help and feedback to players based on their actual gambling behaviour.

Calado, F., Alexandre, J. & Griffiths, M.D. (2017). Youth problem gambling: A cross-cultural study between Portuguese and English youth. Journal of Behavioral Addictions, 6 (Supplement 1), 7.

• Background and aims: In spite of age prohibitions, most re- search suggests that a large proportion of adolescents engage in gambling, with a rate of problem gambling significantly higher than adults. There is some evidence suggesting that there are some cultural variables that might explain the development of gambling behaviours among this age group. However, cross­cultural studies on this field are generally lacking. This study aimed to test a model in which individual and family variables are integrated into a single perspective as predictors of youth gambling behaviour, in two different contexts (i.e., Portugal and England). Methods: A total of 1,137 adolescents and young adults (552 Portuguese and 585 English) were surveyed on the measures of problem gambling, gambling frequency, sensation seeking, parental attachment, and cognitive distortions. Results: The results of this study revealed that in both Portuguese and English youth, the most played gambling activities were scratch cards, sports betting, and lotteries. With regard to problem gambling prevalence, English youth showed a higher prevalence of problem gambling. The findings of this study also revealed that sensation seeking was a common predictor in both samples. However, there were some differences on the other predictors be- tween the two samples. Conclusions: The findings of this study suggest that youth problem gambling and its risk factors appear to be influenced by the cultural context and highlights the need to conduct more cross-cultural studies on this field.

Demetrovics, Z., Richman, M., Hende, B., Blum, K., Griffiths,
M.D, Magi, A., Király, O., Barta, C. & Urbán, R. (2017). Reward Deficiency Syndrome Questionnaire (RDSQ):
A new tool to assess the psychological features of reward deficiency. Journal of Behavioral Addictions, 6 (Supplement 1), 11.

• ‘Reward Deficiency Syndrome’ (RDS) is a theory assuming that specific individuals do not reach a satisfactory state of reward due to the functioning of their hypodopaminergic reward system. For this reason, these people search for further rewarding stimuli in order to stimulate their central reward system (i.e., extreme sports, hypersexuality, substance use and/or other addictive behaviors such as gambling, gaming, etc.). Beside the growing genetic and neurobiological evidence regarding the existence of RDS little re- search has been done over the past two decades on the psychological processes behind this phenomenon. The aim of the present paper is to provide a psychological description of RDS as well as to present the development of the Reward Deficiency Syndrome Questionnaire (developed using a sample of 1,726 participants), a new four-factor instrument assessing the different aspects of reward deficiency. The results indicate that four specific factors contribute to RDS comprise “lack of satisfaction”, “risk seeking behaviors”, “need for being in action”, and “search for overstimulation”. The paper also provides psychological evidence of the association between reward deficiency and addictive disorders. The findings demonstrate that the concept of RDS provides a meaningful and theoretical useful context to the understanding of behavioral addictions.

Demetrovics, Z., Bothe, B., Diaz, J.R., Rahimi­Movaghar, A., Lukavska, K., Hrabec, O., Miovsky, M., Billieux, J., Deleuze,
J., Nuyens, P. Karila, L., Nagygyörgy, K., Griffiths, M.D. & Király, O. (2017). Ten-Item Internet Gaming Disorder Test (IGDT-10): Psychometric properties across seven language-based samples. Journal of Behavioral Addictions, 6 (Supplement 1), 11.

• Background and aims: The Ten-Item Internet Gaming Disorder Test (IGDT-10) is a brief instrument developed to assess Internet Gaming Disorder as proposed in the DSM­5. The first psychometric analyses carried out among a large sample of Hungarian online gamers demonstrated that the IGDT-10 is a valid and reliable instrument. The present study aimed to test the psychometric properties in a large cross-cultural sample. Methods: Data were collected among Hungarian (n = 5222), Iranian (n = 791), Norwegian (n = 195), Czech (n = 503), Peruvian (n = 804), French­speaking (n = 425) and English­ speaking (n = 769) online gamers through gaming­related websites and gaming-related social networking site groups. Results: Confirmatory factor analysis was applied to test the dimensionality of the IGDT-10. Results showed that the theoretically chosen one-factor structure yielded appropriate to the data in all language­based subsamples. In addition, results indicated measurement invariance across all language-based subgroups and across gen- der in the total sample. Reliability indicators (i.e., Cronbach’s alpha, Guttman’s Lambda-2, and composite reliability) were acceptable in all subgroups. The IGDT- 10 had a strong positive association with the Problematic Online Gaming Questionnaire and was positively and moderately related to psychopathological symptoms, impulsivity and weekly game time supporting the construct validity of the instrument. Conclusions: Due to its satisfactory psychometric characteristics, the IGDT-10 appears to be an adequate tool for the assessment of internet gam- ing disorder as proposed in the DSM-5.

Throuvala, M.A., Kuss, D.J., Rennoldson, M. & Griffiths, M.D. (2017). Delivering school-based prevention regarding digital use for adolescents: A systematic review in the UK. Journal of Behavioral Addictions, 6 (Supplement 1), 54.

• Background: To date, the evidence base for school-delivered prevention programs for positive digital citizenship for adolescents is limited to internet safety programs. Despite the inclusion of Internet Gaming Disorder (IGD) as a pro- visional disorder in the DSM-5, with arguable worrying prevalence rates for problematic gaming across countries, and a growing societal concern over adolescents’ digital use, no scientifically designed digital citizenship programs have been delivered yet, addressing positive internet use among adolescents. Methods: A systematic database search of quantitative and qualitative research evidence followed by a search for governmental initiatives and policies, as well as, non­profit organizations’ websites and reports was conducted to evaluate if any systematic needs assessment and/or evidence-based, school delivered prevention or intervention programs have been conducted in the UK, targeting positive internet use in adolescent populations. Results: Limited evidence was found for school-based digital citizenship awareness programs and those that were identified mainly focused on the areas of internet safety and cyber bullying. To the authors’ knowledge, no systematic needs assessment has been conducted to assess the needs of relevant stakeholders (e.g., students, parents, schools), and no prevention program has taken place within UK school context to address mindful and positive digital consumption, with the exception of few nascent efforts by non­profit organizations that require systematic evaluation. Conclusions: There is a lack of systematic research in the design and delivery of school-delivered, evidence-based prevention and intervention programs in the UK that endorse more mindful, reflective attitudes that will aid adolescents in adopting healthier internet use habits across their lifetime. Research suggests that adolescence is the highest risk group for the development of internet addictions, with the highest internet usage rates of all age groups. Additionally, the inclusion of IGD in the DSM-5 as provisional disorder, the debatable alarming prevalence rates for problematic gaming and the growing societal focus on adolescents’ internet misuse, renders the review of relevant grey and published research timely, contributing to the development of digital citizenship programs that might effectively promote healthy internet use amongst adolescents.

Bányai, F., Zsila, A., Király, O., Maraz, A., Elekes, Z., Griffiths, M.D., Andreassen, C.S. & Demetrovics, Z. (2017). Problematic social networking sites use among adolescents: A national representative study. Journal of Behavioral Addictions, 6 (Supplement 1), 62.

• Despite being one of the most popular activities among adolescents nowadays, robust measures of Social Media use and representative prevalence estimates are lacking in the field. N = 5961 adolescents (49.2% male; mean age 16.6 years) completed our survey. Results showed that the one-factor Bergen Social Media Addiction Scale (BSMAS) has appropriate psychometric properties. Based on latent pro le analysis, 4.5% of the adolescents belonged to the at-risk group, who reported low self-esteem, high level of depression and the elevated social media use (34+ hours a week). Conclusively, BSMAS is an adequate measure to identify those adolescents who are at risk of problematic Social Media use and should therefore be targeted by school-based prevention and intervention programs.

Bothe, B., Toth-Király, I. Zsila, A., Griffiths, M.D., Demetrovics, Z. & Orosz, G. (2017). The six-component problematic pornography consumption scale. Journal of Behavioral Addictions, 6 (Supplement 1), 62.

• Background and aims: To our best knowledge, no scale ex- ists with strong psychometric properties assessing problematic pornography consumption which is based on an over- arching theoretical background. The goal of the present study was to develop a short scale (Problematic Pornography Consumption Scale; PPCS) on the basis of Griffiths` (2005) six-component addiction model that can assess problematic pornography consumption. Methods: The sample comprised 772 respondents (390 females; Mage = 22.56, SD = 4.98 years). Items creation was based on the definitions of the components of Griffiths’ model. Results: A confirmatory factor analysis was carried out leading to an 18­item second­order factor structure. The reliability of the PPCS was good and measurement invariance was established. Considering the sensitivity and specificity values, we identified an optimal cut­off to distinguish between problematic and non-problematic pornography users. In the present sample, 3.6% of the pornography consumers be- longed to the at-risk group. Discussion and Conclusion: The PPCS is a multidimensional scale of problematic pornography consumption with strong theoretical background that also has strong psychometric properties.

Dr Mark Griffiths, Professor of Behavioural Addiction, International Gaming Research Unit, Nottingham Trent University, Nottingham, UK

Back tracking: A brief look at using big data in gambling research

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

Further reading

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.

Accentuating the positives: A brief overview of our recent papers on responsible gambling

Following my recent blogs where I outlined some of the papers that my colleagues and I have published on mindfulness, Internet addiction, gaming addiction, youth gambling, and sex addiction, here is a round-up of recent papers that my colleagues and I have published on responsible gambling.

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.

Social responsibility in gambling has become a major issue for the gaming industry. The possibility for online gamblers to set voluntary time and money limits are a social responsibility practice that is now widespread among online gaming operators. The main issue concerns whether the voluntary setting of such limits has any positive impact on subsequent gambling behaviour and whether such measures are of help to problem gamblers. In this paper, this issue is examined through data collected from a representative random sample of 100,000 players who gambled on the win2day gambling website. When opening an account at the win2day site, there is a mandatory requirement for all players to set time and cash-in limits (that cannot exceed 800 <euro> per week). During a 3-month period, all voluntary time and/or money limit setting behaviour by a subsample of online gamblers (n = 5,000) within this mandatory framework was tracked and recorded for subsequent data analysis. From the 5,000 gamblers, the 10 % most intense players (as measured by theoretical loss) were further investigated. Voluntary spending limits had the highest significant effect on subsequent monetary spending among casino and lottery gamblers. Monetary spending among poker players significantly decreased after setting a voluntary time limit. The highest significant decrease in playing duration was among poker players after setting a voluntary playing duration limit. The results of the study demonstrated that voluntary limit setting had a specific and significant effect on the studied gamblers. Therefore, voluntary limits appear to show an appropriate effect in the desired target group (i.e., the most gaming intense players).

Wood, R.T.A., Shorter, G.W. & Griffiths, M.D. (2014). Rating the suitability of responsible gambling features for specific game types: A resource for optimizing responsible gambling strategy. International Journal of Mental Health and Addiction, 12, 94–112.

To date, empirical research relating to responsible gambling features has been sparse. A Delphi-based study rated the perceived effectiveness of 45 responsible gambling (RG) features in relation to 20 distinct gambling type games. Participants were 61 raters from seven countries and included responsible gambling experts (n = 22), treatment providers (n = 19) and recovered problem gamblers (n = 20). The most highly recommended RG features could be divided into three groups: 1) Player initiated tools focused on aiding player behavior; 2) RG features related to informed-player choice; 3) RG features focused on gaming company actions. Overall, player control over personal limits were favoured more than gaming company controlled limits, although mandatory use of such features was often recommended. The study found that recommended RG features varied considerably between game types, according to their structural characteristics. Also, online games had the possibility to provide many more RG features than traditional (offline games). The findings draw together knowledge about the effectiveness of RG features for specific game types. This should aid objective, cost-effective, evidence based decisions on which RG features to include in an RG strategy, according to a specific portfolio of games. The findings of this study will available via a web-based tool, known as the Responsible Gambling Knowledge Centre (RGKC).

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.

Certain gambling operators now provide social responsibility tools to help players gamble more responsibly. One such innovation is the use of pop-up messages that aim to give feedback to the players about the time and money they have thus far spent gambling. Most studies of this innovation have been conducted in laboratory settings, and although controlled studies are indeed more reliable than real-world studies, the non-ecological validity of laboratory studies is still an issue. This study investigated the effects of a slot machine pop-up message in a real gambling environment by comparing the behavioural tracking data of two representative random samples of 400,000 gambling sessions before and after the pop-up message was introduced. The study comprised approximately 200,000 gamblers. The results indicated that, following the viewing of a pop-up message after 1000 consecutive gambles on an online slot machine game, nine times more gamblers ceased their gambling session than did those gamblers who had not viewed the message. The data suggest that pop-up messages can influence a small number of gamblers to cease their playing session, and that pop-ups appear to be another potentially helpful social responsibility tool in reducing excessive play within session.

Auer, M. & Griffiths, M.D. (2014). An empirical investigation of theoretical loss and gambling intensity. Journal of Gambling Studies, 30, 879-887.

Many recent studies of internet gambling—particularly those that have analysed behavioural tracking data—have used variables such ‘bet size’ and ‘number of games played’ as proxy measures for ‘gambling intensity’. In this paper it is argued that the most stable and reliable measure for ‘gambling intensity’ is the ‘theoretical loss’ (a product of total bet size and house advantage). In the long run, the theoretical loss corresponds with the Gross Gaming Revenue generated by commercial gaming operators. For shorter periods of time, theoretical loss is the most stable measure of gambling intensity as it is not distorted by gamblers’ occasional wins. Even for single bets, the theoretical loss reflects the amount a player is willing to risk. Using behavioural tracking data of 100,000 players who played online casino, lottery and/or poker games, this paper also demonstrates that bet size does not equate to or explain theoretical loss as it does not take into account the house advantage. This lack of accuracy is shown to be even more pronounced for gamblers who play a variety of games.

Hanss, D., Mentzoni, R.A., Griffiths, M.D., & Pallesen, S. (2015). The impact of gambling advertising: Problem gamblers report stronger impacts on involvement, knowledge, and awareness than recreational gamblers. Psychology of Addictive Behaviors, 29, 483-491.

Although there is a general lack of empirical evidence that advertising influences gambling participation, the regulation of gambling advertising is hotly debated among academic researchers, treatment specialists, lobby groups, regulators, and policymakers. This study contributes to the ongoing debate by investigating perceived impacts of gambling advertising in a sample of gamblers drawn from the general population in Norway (n = 6,034). Three dimensions of advertising impacts were identified, representing perceived impacts on (a) gambling-related attitudes, interest, and behavior (“involvement”); (b) knowledge about gambling options and providers (“knowledge”); and (c) the degree to which people are aware of gambling advertising (“awareness”). Overall, impacts were strongest for the knowledge dimension, and, for all 3 dimensions, the impact increased with level of advertising exposure. Those identified as problem gamblers in the sample (n = 57) reported advertising impacts concerning involvement more than recreational gamblers, and this finding was not attributable to differences in advertising exposure. Additionally, younger gamblers reported stronger impacts on involvement and knowledge but were less likely to agree that they were aware of gambling advertising than older gamblers. Male gamblers were more likely than female gamblers to report stronger impacts on both involvement and knowledge. These findings are discussed with regard to existing research on gambling advertising as well as their implications for future research and policy-making

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.

Over the last few years, there have been an increasing number of gaming operators that have incorporated on-screen pop-up messages while gamblers play on slot machines and/or online as one of a range of tools to help encourage responsible gambling. Coupled with this, there has also been an increase in empirical research into whether such pop-up messages are effective, particularly in laboratory settings. However, very few studies have been conducted on the utility of pop-up messages in real-world gambling settings. The present study investigated the effects of normative and self-appraisal feedback in a slot machine pop-up message compared to a simple (non-enhanced) pop-up message. The study was conducted in a real-world gambling environment by comparing the behavioral tracking data of two representative random samples of 800,000 gambling sessions (i.e., 1.6 million sessions in total) across two conditions (i.e., simple pop-up message versus an enhanced pop-up message). The results indicated that the additional normative and self-appraisal content doubled the number of gamblers who stopped playing after they received the enhanced pop-up message (1.39%) compared to the simple pop-up message (0.67%). The data suggest that pop-up messages influence only a small number of gamblers to cease long playing sessions and that enhanced messages are slightly more effective in helping gamblers to stop playing in-session.

Wood, R.T.A. & Griffiths, M.D. (2015). Understanding positive play: An exploration of playing experiences and responsible gambling practices. Journal of Gambling Studies, 31, 1715-1734.

This study is one of the first to explore in detail the behaviors, attitudes and motivations of players that show no signs of at-risk or problem gambling behavior (so-called ‘positive players’). Via an online survey, 1484 positive players were compared with 209 problem players identified using the Lie/Bet screen. The study identified two distinct groups of positive players defined according to their motivations to play and their engagement with responsible gambling (RG) practices. Those positive players that played most frequently employed the most personal RG strategies. Reasons that positive players gave for gambling were focused on leisure (e.g., playing for fun, being entertained, and/or winning a prize). By contrast, problem gamblers were much more focused upon modifying mood states (e.g., excitement, relaxation, depression and playing when bored or upset). The present study also suggests that online gambling is not, by default, inherently riskier than gambling in more traditional ways, as online gambling was the most popular media by which positive players gambled. Furthermore, most positive players reported that it was easier to stick to their limits when playing the National Lottery online compared to traditional retail purchasing of tickets. Problem players were significantly more likely than positive players to gamble with family and friends, suggesting that, contrary to a popular RG message, social play may not be inherently safer than gambling alone. It is proposed that players (generally) may identify more with the term ‘positive play’ than the term ‘RG’ which is frequently interpreted as being aimed at people with gambling problems, rather than all players.

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.

Over the last few years, online gambling has become a more common leisure time activity. However, for a small minority, the activity can become problematic. Consequently, the gambling industry has started to acknowledge their role in player protection and harm minimization and some gambling companies have introduced responsible gambling tools as a way of helping players stay in control. The present study evaluated the effectiveness of mentor (a responsible gambling tool that provides personalized feedback to players) among 1,015 online gamblers at a European online gambling site, and compared their behavior with matched controls (n = 15,216) on the basis of age, gender, playing duration, and theoretical loss (i.e., the amount of money wagered multiplied by the payout percentage of a specific game played). The results showed that online gamblers receiving personalized feedback spent significantly less time and money gambling compared to controls that did not receive personalized feedback. The results suggest that responsible gambling tools providing personalized feedback may help the clientele of gambling companies gamble more responsibly, and may be of help those who gamble excessively to stay within their personal time and money spending limits.

Dr. Mark Griffiths, Professor of Behavioural Addiction, International Gaming Research Unit, Nottingham Trent University, Nottingham, UK

Further reading

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. (2014). Personalised feedback in the promotion of responsible gambling: A brief overview. Responsible Gambling Review, 1, 27-36.

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., Schneeberger, A. & Griffiths, M.D. (2012). Theoretical loss and gambling intensity: A simulation study. Gaming Law Review and Economics, 16, 269-273.

Griffiths, M.D. (2012). Internet gambling, player protection and social responsibility. In R. Williams, R. Wood & J. Parke (Ed.), Routledge Handbook of Internet Gambling (pp.227-249). London: Routledge.

Griffiths, M.D. (2014). The use of behavioural tracking methodologies in the study of online gambling. SAGE Research Methods Cases. Located at: http://dx.doi.org/10.4135/978144627305013517480

Griffiths, M.D. & Auer, M. (2011). Approaches to understanding online versus offline gaming impacts. Casino and Gaming International, 7(3), 45-48.

Luccini, F. & Griffiths, M.D. (2015). Preventing and treating problem gamblers in Italy. Responsible Gambling Review, 2, 20-26.

Wood, R.T.A. & Griffiths, M.D. (2014). Putting responsible gambling, theory and research into practice. Responsible Gambling Review, 1, 1-5.

Wood, R.T.A., Shorter, G. & Griffiths, M.D. (2014). Selecting the right responsible gambling features, according to the specific portfolio of games. Responsible Gambling Review, 1(1), 51-63.

Watch this space: Another look at box-set bingeing

Regular readers of my blog will know that I have both a professional and personal interest in ‘box set binging’ – people like myself who sit and watch a whole television series at once either on DVD or on television catch-up services (see my two previous articles on the topic here and here). In my previous blogs on the topic I noted there was a lack of published academic research on the topic. However, a new study on the phenomenon – ‘Just one more episode’: Frequency and theoretical correlates of television binge watching’ – has just been published by Emily Walton-Pattison and her colleagues in the Journal of Health Psychology. The paper argues that binge watching may have detrimental health implications and that binge watching has impulsive aspects. As the authors noted in their paper:

“With the emergence of online streaming television services, watching television has never been so easy and a new behavioural phenomenon has arisen: television binge watching, that is, viewing multiple episodes of the same television show in the same sitting. Watching television is the most widespread leisure-time sedentary activity in adults (Wijndaele et al., 2010), involving little metabolic activity (Hu et al., 2003). In the United Kingdom, over one-third of adults spend at least four hours a day watching television (Stamatakis et al., 2009). Up to 33% of men and 45% of women in the United Kingdom fail to achieve recommended physical activity levels (Craig and Mindell, 2014). As lack of physical activity is the fourth leading mortality risk factor (World Health Organization, 2010), identifying factors that pre- vent achieving health-protective levels of physical activity remains important Furthermore, sedentary behaviour is linked with adverse health outcomes independently of physical activity (Veerman et al., 2012). Time spent watching television is also linked with obesity and reduced sleep time (Vioque et al., 2000). Understanding the factors that lead to watching television at ‘binge’ levels may help to target interventions to reduce sedentary activity and obesity rates and improve sleep hygiene”.

The study involved 86 people who completed an online survey that assessed (among other things) outcome expectations (assessed via six attitudinal items such as ‘Watching more than two episodes of the same TV show in the same sitting over the next 7 days will lead me to be physically healthier’), proximal goals (assessed via one question ‘On how many days do you intend to watch more than two episodes of the same TV show in the same sitting over the next 7 days?’), self-efficacy (assessed via five attitudinal items such as ‘I am confident that I can stop myself from watching more than two episodes of the same TV show if I wanted to’), anticipated regret (assessed via two items – ‘If I watched more than two episodes of the same TV show in the same sitting in the next 7 days, I would feel regret’ and ‘If I watched more than two episodes of the same TV show in the same sitting in the next 7 days I would later wish I had not’), goal conflict (with two items such as ‘How often does it happen that because of watching more than two episodes of the same TV show in the same sitting, you do not invest as much time in other pursuits as you would like to?’), goal facilitation (assessed via three items such as ‘Watching more than two episodes of the same TV show in the same sitting in the next 7 days will help/facilitate my participation in regular physical activity’), and self-reported binge watching over the last week (defined as “watching more than two episodes of the same TV show in one sitting”), as well as noting various demographic details (age, gender, marital status, number of children, and body mass index).

The study found that their participants reported binge watching at least once a week (an average of 1.42 days/week) and that binge watching was predicted most by intention and outcome expectations. Automaticity, anticipated regret, and goal conflict also contributed to binge watching. Based on their results, the authors noted:

“The findings have implications for theory development and intervention…The role of automaticity suggests that interventions aiming to address problematic binge watching (e.g. due to increased sedentary activity) could consider techniques that address automaticity. For example, some online streaming services include in-built interruptions after a number of consecutive episodes have been viewed. There would be opportunities to harness these interruptions. Goal conflict findings indicated that participants who reported more binge watching also reported that binge watching undermined other goal pursuits. Linking such findings to an intervention addressing anticipated regret could provide a useful opportunity…Drawing upon the addiction literature in relation to other types of binge behaviours may further refine potential appetitive and loss of control features that may extend from addictive behaviours with a binge potential, such as eating, sex and drugs, to binge watching”.

Obviously the study relied on self-reports among a small sample of television viewers but given that this is the first-ever academic study of binge watching, it provides a basis for further research to be carried out. As in my own research into gambling where we have begun to use tracking data provided by gambling companies, the authors also note that such objective measures could also be used in the field of researching into television binge watching:

“[Future research] could include using objective measures of binge watching including ecological momentary assessment, ambient sound detection, recording and/or partnering with streaming firms or software-based monitoring. Further insight into binge watching could make a distinction between television show-specific factors, such as genre, length, real-time versus on-demand services, as well as contextual factors (e.g., where binge watching occurred, with whom and when) and assess the association between binge watching and health outcomes including physical activity, eating and sleep hygiene”.

This is one of the first times I can end one of my articles by saying that this is literally a case of “watch this space”!

Dr Mark Griffiths, Professor of Behavioural Addictions, International Gaming Research Unit, Nottingham Trent University, Nottingham, UK

Further reading

Bates, D. (2015). Watching TV box-set marathons is warning sign you’re lonely and depressed – and will also make you fat. Daily Mail, January 29. Located at: http://www.dailymail.co.uk/health/article-2931572/Love-marathon-TV-session-warning-sign-lonely-depressed.html

Craig, R. & Mindell, J. (2014). Health Survey for England 2013. London: The Health & Social Care Information Centre.

Daily Edge (2014). 11 signs of you’re suffering from a binge-watching problem. Located at: http://www.dailyedge.ie/binge-watching-problem-signs-1391910-Apr2014/

Griffiths, M.D. (1995). Technological addictions. Clinical Psychology Forum, 76, 14-19.

Hu, F.B., Li, T.Y., Colditz, G.A., et al. (2003) Television watching and other sedentary behaviors in rela- tion to risk of obesity and type 2 diabetes mellitus in women. JAMA, 289, 1785–1791.

Kompare, D. (2006). Publishing flow DVD Box Sets and the reconception of television. Television & New Media, 7(4), 335-360.

Spangler, T. (2013). Poll of online TV watchers finds 61% watch 2-3 episodes in one sitting at least every few weeks. Variety, December 13. Located at: http://variety.com/2013/digital/news/netflix-survey-binge-watching-is-not-weird-or-unusual-1200952292/

Stamatakis, E., Hillsdon, M., Mishra, G., et al. (2009) Television viewing and other screen-based entertainment in relation to multiple socioeconomic status indicators and area deprivation: The Scottish Health Survey 2003. Journal of Epidemiology & Community Health, 63, 734–740.

Sussman, S., & Moran, M.B. (2013). Hidden addiction: Television. Journal of Behavioral Addictions, 2(3), 125-132.

Veerman, J.L., Healy, G.N., Cobiac, L.J., et al. (2012) Television viewing time and reduced life expec- tancy: A life table analysis. British Journal of Sports Medicine, 46, 927–930.

Vioque, J., Torres, A. & Quiles, J. (2000) Time spent watching television, sleep duration and obesity in adults living in Valencia, Spain. International Journal of Obesity, 24, 1683–1688.

Walton-Pattison, E., Dombrowski, S.U. & Presseau, J. (2016). ‘Just one more episode’: Frequency and theoretical correlates of television binge watching. Journal of Health Psychology, doi:1359105316643379

Wijndaele, K., Brage, S., Besson, H., et al. (2010) Television viewing time independently predicts all-cause and cardiovascular mortality: The EPIC Norfolk study. International Journal of Epidemiology, 40, 150–159.

Inter-bet gambling: The psychology of online sports betting

Until the early 2000s, there appeared to be a commonly held perception that consumers viewed the Internet as an information gathering tool rather than a place to spend money. The explosive growth in online gambling and betting shows this is no longer true. For me, one of the interesting questions is how gaming companies use the psychology of people who like to gamble on sports events to get them to access sports betting sites (especially if it is done in a socially responsible way that enhances the punter’s experience rather than exploits them).

Trust and reliability: Let’s look at sports betting from an individual level. A sports fan has logged on to the Internet and is in the process of deciding which online sports betting website to make a beeline for. What kinds of things influence their decision? A recommendation from one of their friends? Advice from a gambling portal? An advert they saw in a magazine? From a psychological perspective, research on how and why people access particular commercial websites indicates that one of the most important factors is trust. If people know and trust the name, they are more likely to use that service. Reliability is also a related key factor. Research shows that many people (including sports bettors) still have concerns about Internet security and may not be happy about putting their personal details online. But if there is a reliable offline branch nearby, it gives them an added sense of security (i.e., a psychological safety net). For some people, trust and security issues will continue to be important inhibitors of online gambling. Punters need assurance and compelling value propositions from trusted gaming operators and operators to overcome these concerns.

Personalization: One of the growth areas in e-commerce has been personalization and most online commercial organisations now have a personalization strategy as part of its business plan. However, this practice is a double-edged sword that can prove to be a large logistical problem for companies who use such a strategy. Tracking every move for marketing purposes is one thing. Using these data for personalization purposes can sometimes prove troublesome. The amount of data is potentially enormous. Producing personalized pages for everyone is also logistically difficult and may even turn potential punters away. The key is knowing what to ask the punter. Those in the gaming industry have to think intelligently and creatively about what to ask their customers in a way that the information gained can be used effectively. Attracting customers and providing recommendations relies on the those in the gaming industry putting punters first. Integration can also be a factor here. The industry has to think of creative ways to make the website experience more personal.

Imprinting: One of the most important marketing strategies that companies engage in is “imprinting” new customers. Online punters quickly adopt predictable Internet usage patterns and evidence suggests that they don’t switch online allegiances easily. Smart gaming operators will work at becoming a starting point for the novice gambler and capitalize on this opportunity for capturing player loyalty. The emerging post-teenage market is a key consideration although from a social responsibility perspective thought needs to be given so that teenagers are not exploited. There is a whole Internet generation of people coming through who have a positive outlook on online commercial activities. They may be happier to enter credit card details online and/or meet others online. This has the potential to lead to major clientele changes as the profiles of these people may be radically different from previous punters. The problem is that the young don’t tend to have much disposable income and are less likely to own credit cards. Therefore, another market segment that operators need to target to are the over-50s who are starting to use the Internet for shopping and entertainment use. Early retirees have both time and money. This is why gaming operators need to strategically target the ‘grey pound.’

Contextual commerce: So what can operators do next? Contextual commerce may be one avenue that gaming operators will need to go down. In most retail outlets, shoppers notice what other people are buying and this may influence the purchaser’s choice. Companies are now using software that allows customers to do this online including interacting with other like-minded people. Seeing what everyone else is betting on may influence the decision process. There is also the potential to bring in techniques used on home television shopping channels. Presenters tell viewers how much of a product has been sold with viewers to instil a sense of urgency into the buying process, along with an element of peer review. This could be applied by gaming operators if people are gambling as part of a sports betting community.

Getting the balance right on the chance-skill dimension: 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 gamblers. Games of chance (like lotteries) offer no significant edge to sports gamblers and are unlikely to be gambled upon. While games of skill provide a significant edge for the gambler, serious gamblers need more than an edge – they often need an opponent who can be exploited (which helps explain the popularity of online poker). Serious gamblers 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.

Inter-gambler competition and the exercise of skill: Over the last few years I have often been asked by the media about the increasing popularity of online sports betting, particularly in relation to betting exchanges. Psychologists claim that male gamblers are attracted to sports betting because they love competitiveness. Sports bettors clearly feel that gambling via betting exchanges provides value for money and an opportunity to exercise their skill. Another important factor that I feel is really important in the rise of sports betting is not just the inherent competiveness but also the inter-gambler competition. Obviously there is an overlap between competitiveness and skill but they are certainly not the same and operators need to show how the sites they recommend feed into the psychological needs and desires of the sports bettor.

I’m sure many people’s view of psychology is that it is little more than common sense (and to be honest, some of it is). However, I hope that some of what I had to offer in the rest of this blog was more than just common sense.

Dr Mark Griffiths, Professor of Gambling Studies, International Gaming Research Unit, Nottingham Trent University, Nottingham, UK

Further reading

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.

Griffiths, M.D. (2005). Online betting exchanges: A brief overview. Youth Gambling International, 5(2), 1-2.

Griffiths, M.D. (2007). Brand psychology: Social acceptability and familiarity that breeds trust and loyalty. Casino and Gaming International, 3(3), 69-72.

Griffiths, M.D. (2009). Social responsibility in gambling: The implications of real-time behavioural tracking. Casino and Gaming International, 5(3), 99-104.

Griffiths, M.D. & Whitty, M.W. (2010). Online behavioural tracking in Internet gambling research: Ethical and methodological issues. International Journal of Internet Research Ethics, 3, 104-117.

McCormack. A. & Griffiths, M.D. (2012). What differentiates professional poker players from recreational poker players? A qualitative interview study. International Journal of Mental Health and Addiction, 10, 243-257.

Parke, A., Griffiths, M.D. & Irwing, P. (2004). Personality traits in pathological gambling: Sensation seeking, deferment of gratification and competitiveness as risk factors, Addiction Research and Theory, 12, 201-212.

Recher, J. & Griffiths, M.D. (2012). An exploratory qualitative study of online poker professional players. Social Psychological Review, 14(2), 13-25.

Wood, R.T.A. & Griffiths. M.D. (2008). Why Swedish people play online poker and factors that can increase or decrease trust in poker websites: A qualitative investigation. Journal of Gambling Issues, 21, 80-97.

Sell division: The use of technology in commercial marketing

Today’s blog is only loosely connected to the types of behaviour that I usually cover but relates to the issue of excessive technological use. However, rather than focusing on the individual, my article today focuses on corporate technological excess, and more specifically the seemingly excessive use of technological marketing. Technology continues to invade almost every area of our lives. Although the advantages of these technologies significantly outweigh the disadvantages, technology is increasingly being used in commercial settings that some citizen’s rights groups claim are exploitative, unethical, and border on the criminal.

Shopping loyalty cards are now an every day part of consumer behaviour. Most people probably don’t stop to think about the reasons behind their introduction but they have the potential to be exploitative. In short, loyalty cards track every purchase a consumer makes over a long period (often years) including the store shopped at, the date and time, and the price paid. The long tracking period allows for monitoring of trends in purchasing. Stores may also record the method of payment, whether the card was swiped or keyed into the till, and the checkout that was used. The supermarkets use these data to categorize customers. In addition to the data provided when the loyalty card was issued, commercial operators can draw conclusions from the address using sophisticated categorization systems. Many companies now use customer relationship marketing software to help make sense and synthesize the information gathered. Ever since they were introduced, underhand uses of loyalty cards have been mooted. As long ago as 1999, the UK Ministry of Agriculture suggested cross checking purchases of genetically modified food with health records, effectively making the cards part of a huge medical experiment. However, the supermarkets declined to take part.

It’s probably a fair assumption to make that the online population views the Internet as much a tool for information gathering and communication as for commercial transactions. The most powerful impacts are social rather than commercial. Just like the companies that run loyalty card schemes, Internet service providers can record when you logged on and off, how many seconds the connection lasted, and the Internet protocol address allocated during the session. They can also compile a detailed e-mail history. This can contain the header information from every e-mail received by the account in the period, the return address provided by the sender, the ISP from which the e-mail originated, the date and the time of the sending, an ID code, and the title of the e-mail. Most people have no idea how much potential there is for invasion of privacy.

There are some areas that are potentially more worrying than others. Take the case of online gambling (that I have covered in previous blogs relating to behavioural tracking). When it comes to gambling, there is a very fine line between providing what the customer wants and exploitation. The gaming industry now sells gambling in much the same way that any other business sells things. On joining Internet gambling sites, players supply lots of information including name, address, telephone number, date of birth, and gender. Internet gambling operators will know the player’s favourite game and the amounts they have wagered. Basically they can track the playing patterns of any gambler. They will know more about the gambler’s playing behaviour than the gamblers themselves. They will be able to send the gambler offers and redemption vouchers, complimentary accounts, etc. All of these things are introduced to supposedly enhance customer experience. Benefits and rewards to the customer include cash, food and beverages, entertainment and general retail. However, more unscrupulous operators are able to entice high spending gamblers (some of which will be problem gamblers) back onto their sites with tailored freebies (such as the inducement of ‘free’ bets and bonuses).

The Internet also appears to be a rapidly growing medium for child-oriented marketing with sites ranging from Pokemon and Barbie to Lego. These sites provide what appears to be a safe environment for children to play in online (and something my own children used to do). Today’s children are computer literate and the Internet empowers them to influence what they want for Christmas or their birthday. However, how ethical is it for businesses to use advertising to pitch to children – individuals who in most other spheres (e.g., voting, sex, legal documents) are treated as incapable of making decisions. Many claim the adverts carry a similar message (i.e., “If you haven’t got this product, you are abnormal”). The aim of most marketing is to sell goods, but adverts aimed at children are designed to get them to pressurize their peers and parents.

A number of years ago, the US Center for Media Education (CME) claimed that advertisers and marketeers exploit children by advertising products on the Internet in ways that manipulate children and violate their privacy. They urged the US Federal Trade Commission to develop safeguards for children and claimed that these advertisements would infringe American regulations that put safeguards on broadcast media like the television. They recommend that there should be no children’s content directly linked to advertising and that direct interaction between children, and that product spokescharacters (such as Kellogs ‘Tony the Tiger’) should not be allowed.

The CME claimed advertisers used a variety of online methods (such as ‘infomercials’) to collect detailed data and compile individual child profiles. This information they claimed, was used to establish direct and intimate relationships with children online. The CME claimed children’s privacy is routinely threatened to encourage children to disclose personal information about themselves and their families with some sites offering gifts and prizes. This technology makes it possible to monitor every interaction between the child and the advertisement allowing firms to create personalized marketing for a child. Again, questions need to be asked about how far advertisers can go and what protection vulnerable groups should have.

Other new technologies are also making an impact – often without the person’s knowledge. For instance, television set top boxes can monitor viewer activities. Those who operate set top boxes say they are doing it in order to develop personalized advertising. However, many claim that Internet video providers should not be able to track and sell information about what you viewers are watching in the privacy of your own home. Companies who use these systems claim they only keep anonymous viewing information. They also stress that their viewers can opt out of the data collection but the reality is that very few do. Perhaps the best way forward is to see the introduction of ‘opt-in’ rather than opt-out clauses.

Given the increasingly sophisticated technology on offer, companies have to do a lot of planning to get most out of their databases. There are two main sorts of database. The first type is a ‘flat file’ databases with information on them, set one after the other. The second type is a ‘relational database’ that can build relationships between different fields of information. For instance, if a company wanted to find a number of customers who had bought a particular product from them in the last month or sort their customers by their address, a flat file database could do those tasks individually whereas relational databases would do it simultaneously. The really creative part (and some might say potentially exploitative and unethical part) in database use starts when companies begin to look seriously at what the information can do for them. This is the stage when companies start to ask intelligent questions about the actual purpose of the information they have gathered.

Over the two decades, customer relationship management (CRM) has become integrated with database management. Companies offer insight into CRM processes that become available when information is managed electronically. Analytical CRM is geared towards understanding a series of interactions with customers over time in activity-based terms, with a view to understanding whether a given customer is profitable to the company and satisfied with the quality of that relationship. For this to work, the company needs in-depth detail on finance, human resources, distribution, and manufacturing so that they know exactly what a customer is costing them. Companies then know whether the customer is worth hanging on to. While the majority of companies may be using CRM for genuine customer enhancement, there are always those who are less scrupulous and may use such information to exploit.

Even a brief examination of how technology is being used in commercial situations demonstrates that the potential for exploitation of the customer is ever present and that such technologies should be monitored closely. Whether any of these practices will be seen as in some way criminal in the future remains to be seen.

Dr Mark Griffiths, Professor of Gambling Studies, International Gaming Research Unit, Nottingham Trent University, Nottingham, UK

Further reading

Delfabbro, P.H., King, D.L & Griffiths, M.D. (2012). Behavioural profiling of problem gamblers: A critical review. International Gambling Studies, 12, 349-366.

Griffiths, M.D. (2003). Exploitation and fraud on the Internet: Some common practices, The Criminal Lawyer, 132, 5-7.

Griffiths, M.D. (2003). Dot cons: Exploitation and Fraud on the Internet (Part 2). The Criminal Lawyer, 134, 3-5.

Griffiths, M.D. (2005).  Exploitative, unethical, criminal? The use of technology in commercial marketing. Justice of the Peace, 169, 916-917.

Griffiths, M.D. (2008). Digital impact, crossover technologies and gambling practices. Casino and Gaming International, 4(3), 37-42.

Griffiths, M.D. (2009). Social responsibility in gambling: The implications of real-time behavioural tracking. Casino and Gaming International, 5(3), 99-104.

Griffiths, M.D. (2010). Social responsibility in marketing for online gaming affiliates. i-Gaming Business Affiliate, June/July, p.32.

Griffiths, M.D. (2011). Online behavioural tracking: Identifying problem gambling. World Online Gambling Law Report, 10(5), 10-11.

Griffiths, M.D. (2013). Responsible marketing and advertising of gambling. i-Gaming Business Affiliate, August/September, 50.

Griffiths, M.D. & Whitty, M.W. (2010). Online behavioural tracking in Internet gambling research: Ethical and methodological issues. International Journal of Internet Research Ethics, 3, 104-117.

Zangeneh, M., Griffiths, M.D. & Parke, J. (2008). The marketing of gambling. In Zangeneh, M., Blaszczynski, A., and Turner, N. (Eds.), In The Pursuit Of Winning (pp. 135-153). New York: Springer.

Gaming desire: Online behavioural tracking in Facebook

In a previous blog I examined how researchers in the gambling studies field are using online behavioural tracking data to study excessive gambling. However, online tracking data is used all the time in lots of contexts by lots of companies to better understand human behaviour. Most people view the internet as a very anonymous medium but rest assured, someone, somewhere, is monitoring everything you do online. The internet is the most non-anonymous place I can possibly think of.

Data from online environments provide potential advantages to many sectors (particularly those in the fields of marketing and advertising). This is because every click and transaction of every customer is automatically stored in a database and provides very high data quality for those who know what to do with it. This is very much in contrast to market surveys where such data have to be gathered at high costs, with lots of manual work involved, and may be prone to error. Within my own gambling studies field, a number of different teams across the world (including my own) have now been given access to behavioural tracking data of gamblers. Such data include:

• Account data (i.e., general information about each player) including user ID, date of birth, gender, other demographic data (if available), and geographic data (e.g., postal codes).
• Game data (for each player) including game ID, game type (if available), amount of winnings, amount of money spent, number of bets/spins (or equivalent), chasing behaviour, bonus usage, and session time.
• Financial data (for each player, and game, if applicable) including the number and amount of deposits, the number of money withdrawals, and the number of accounts.
• Responsible gaming limit data (i.e., time, spending, etc) for each player, and game (if applicable) including current time and spend limits, changes of limits, and the number of limit strikes (i.e., when players reach their pre-determined limit for deposits or time.).
• Miscellaneous data for each player (and game if applicable) including the length of playing session, login information, number of ‘cool-off periods’ (when players lock themselves out of their accounts for pre-defined periods of time), and any other data that might be relevant or of interest.

Many of these types of data are also held by those who operate games via social networking sites and could potentially be used for research purposes. Online social networking sites (e.g., Facebook) typically collect two types of information. These are (i) personal details provided by a user (e.g., name, email address, telephone number, address, gender and schools attended, etc.) and (ii) usage data collected automatically as the user clicks from one webpage to another (e.g., how users access the site, type of web browser used, time spent logged onto the site, etc.). Other day-to-day types of data collected from social network users include:

• The IP address used every time a user logs on to the social networking site.
• Every friend request a user has ever received and how they responded.
• A list of all users’ family and friends (including relationship status)
• Every pressing of ‘Like’ that a user has used on a wide variety of topics and events.
• Credit card information, if the user ever purchased credits or advertising on the social networking site.
• Every poke a user has exchanged (including both pokes made by the user on someone else and a record of all the times a user has been poked).
• Every event a user has been invited to through via the networking site and how they responded.
• Dates of user name changes and historical privacy settings changes.
• Photographs of life events and camera metadata including time stamps and latitude/longitude of picture location, as well as tags from photos – even if users have untagged them.
• The last known physical location of the user, with latitude, longitude, time/date, altitude, and more.
• A history of every private and public message and chat on the social networking site.
• Lists of every song the user has listened to online and every article that they have read.

Almost any variable (beyond the usual demographic ones such as age, gender, education, relationship status, etc.) could be examined as a risk and/or protective factor in the context of social gaming (e.g., Is the number of credit cards registered related to time and/or money spent on social gaming? How often does a person access a social game following the recommendation of another person? Does the type of social gaming differ by geographical location of where the user logged on? Does poking facilitate social gaming?). Facebook currently stores up to 800 pages of data on each of its users.

It has also been revealed that some social networking sites (e.g., Facebook) collect data from users who have logged out (usually – say the company – “for security purposes”) through the use of ‘cookies’. Cookies relay information from any site a user visits that contains a link to the social networking site such as a ‘like’ button.  With these data, the company can map out any users more general web usage.

Access to such data sets may provide insights into a number of important areas such as transitions from social gaming to social gambling, and how social gaming might be integrated with other online behaviours (such as gambling). However, other types of research are also needed including longitudinal studies so that people can be tracked over time (either via behavioural tracking and/or surveys), and more detailed qualitative work (interviews and focus groups) with different player types (e.g., regular and/or problematic gamblers/social gamers).

Dr Mark Griffiths, Professor of Gambling Studies, International Gaming Research Unit, Nottingham Trent University, Nottingham, UK

Further reading

Delfabbro, P.H., King, D.L & Griffiths, M.D. (2012). Behavioural profiling of problem gamblers: A critical review. International Gambling Studies, 12, 349-366.

Griffiths, M.D. (2003). Internet gambling: Issues, concerns and recommendations. CyberPsychology and Behavior, 6, 557-568.

Griffiths, M.D. (2009). Social responsibility in gambling: The implications of real-time behavioural tracking. Casino and Gaming International, 5(3), 99-104.

Griffiths, M.D. (2013). Social gambling via Facebook: Further observations and concerns. Gaming Law Review and Economics, 17, 104-106.

Griffiths, M.D. & Parke, J. (2002). The social impact of internet gambling. Social Science Computer Review, 20, 312-320.

Griffiths, M.D. & Whitty, M.W. (2010). Online behavioural tracking in Internet gambling research: Ethical and methodological issues. International Journal of Internet Research Ethics, 3, 104-117.

Griffiths, M.D. & Wood, R.T.A. (2008). Gambling loyalty schemes: Treading a fine line? Casino and Gaming International, 4(2), 105-108.

Kuss, D.J. & Griffiths, M.D. (2011). Online social networking and addiction: A literature review of empirical research. International Journal of Environmental and Public Health, 8, 3528-3552.

Loss leaders: What is the best way to measure ‘gambling intensity’?

The issue of how to measure ‘gambling intensity’ is an important one in the gambling studies field. Gambling intensity is one of those concepts that means different things to different researchers but basically refers to how absorbed gamblers are based on the time and money they spend gambling. Over the last few years, this issue has become much more to the fore as researchers in various jurisdictions have been given access to behavioural tracking data (i.e., actual data showing what online gamblers actually do online such as the games they are playing, the time they spend online, the amount of money that they spend, etc.). This has initiated a whole new line of gambling research that is already providing insights about gambling that we never had before.

Many of these studies have used proxy measures for gambling intensity including variables such ‘bet size’ and ‘number of games played’. Another major problem with these studies is that they have tended to present data by single game type (e.g., only data from online poker players or sports bettors are presented). However, as researchers such as myself have noted, online gamblers typically gamble on a variety of games.

There are various ways to conceptualize gambling intensity. Such ways could include parameters involving the time spent gambling, the number of gambles made, and/or the amount of money won or lost while gambling. In almost all of the studies carried out to date, monetary involvement has tended to be the main proxy used measure for gambling intensity. However, I and my colleague Michael Auer have proposed a different proxy measure for the money risked while gambling. We define gambling intensity as the amount of money that players are putting at risk when playing. This might be considered easy to do (e.g., by using ‘bet size’), but the element of chance is rarely accounted for, especially when a random win occurs. For instance, two gamblers putting the same amount of money at risk might end up with very different wins or losses at the end of similar length gambling sessions because of the chance factor. For this reason, we are now using a measure that is completely independent of random events and takes into account the true amount of money that players are prepared to risk. The interesting aspect of this is that most of the time, gamblers themselves are probably not aware of the amount of money they risked at the end of a playing session.

Our first published paper in this area was a simulation study published last year in the journal Gaming Law Review and Economics. In that paper, we demonstrated that the most robust and stable measure for ‘gambling intensity’ is what we call the ‘theoretical loss’. Our fiest paper on this topic showed that all previous studies using proxy measures for ‘gambling intensity’ had failed to take into account the house advantage. Outcomes in games of chance over the long-term will always be dependent upon the house advantage of each different type of game. Dr. S. Li showed in a 2003 paper published in the Journal of Risk Research that ‘at risk’ decision-making in the short-term is totally different from decision-making over longer periods of time. Decision making over the long-term can be explained by the expected value whereas short-term decision-making does not seem to be based on any expectation rule. However, studies investigating decision-making in situations where people have to make choices assume that players have a real choice in which they can truly influence the outcome and (thus) the expected return. However, this is not the case in pure chance games. Whatever the player chooses to do in pure chance situations, the house advantage will determine the expected return in the long-term.

As we pointed out in our 2012 paper, games with a high house advantage lead to higher player losses and games with a low house advantage lead to lower player losses. Theoretical loss is the same measure that the gaming industry describes as Gross Gaming Revenue (GGR), and is the difference between ‘Total Bet’ and ‘Total Win’. The ‘theoretical loss’ of any given game is represented by the product of the bet size and the house advantage. Over very long periods of time, the theoretical loss corresponds to the GGR with increasing accuracy. The more diverse the gambling behaviour, the more that bet size deviates from the theoretical loss.

By incorporating the theoretical loss, the amount risked can be measured at a very detailed level. For instance, French roulette has a house advantage of 2.7% and keno has a house advantage of 10%. This means that a player who repeatedly bets $100 on roulette will end up with a loss of $2.7, and a player who repeatedly bets $100 on keno will end up with a loss of $10. Therefore, the product of bet size and theoretical loss represents the amount of money that player will lose in the long run. Previous studies that have used bet size (as a proxy measure for gambling intensity) would assign the same gambling of $10 intensity to the two players in the aforementioned example (and which obviously is not the case). The bet size is the one risk parameter that players are most likely to be aware of during gambling. However, it is deceptive as it does not take into account the expected return/loss that is controlled by the gaming operator via their house advantage.

Our simulation study of 300,000 online gamblers showed that bet size explained only 56% of the variance of the theoretical loss, and the number of games played explained 32% of the variance of theoretical loss. This means that when using bet size alone, 44% of the gambling behaviour remains unexplained. When using the number of games played alone, 68% of the variance is left unexplained. As this study was a simulation, we recently replicated our first study using real online gambler behavioural tracking data. There are many advantages and disadvantages with using data collected via behavioural tracking. However, the main advantages are that behavioural tracking data (a) provide a totally objective record of an individual’s gambling behaviour on a particular online gambling website, (b) provide a record of events and can be revisited after the event itself has finished, and (c) usually comprise very large sample sizes.

Our latest study on theoretical loss in the Journal of Gambling Studies comprised 100,000 online gamblers who played casino, lottery or poker games during a one-month period on the Austrian win2day gambling website. All games played by these gamblers were recorded and subsequently analysed. The game types were categorized into eight distinct groups: (i) Lottery – Draw/Instant, (ii) Casino – Card, (iii) Casino – Slot, (iv) Casino – Videopoker, (v) Casino – Table, (vi) Casino Other, (vii) Bingo and (viii) Poker. For each of the game types and each player, the ‘bet size’ and the ‘theoretical loss’ were computed for the recorded time period. In terms of house advantage these game types are very different. In general, lottery games have a relatively high house advantages (typically 50%) whereas slot machines have house advantages in the range of 1 to 5% depending on the gaming platform and the specific game. Poker on the other hand does not have a house advantage as such. In poker, the gaming involvement can be measured via the rake. The rake is a fixed percentage of the stake (bet size) that goes to the casino. The overall theoretical loss is thus comprised of the theoretical loss across all game types plus the poker rake.

Although we found a high correlation between the ‘bet size’ and the overall ‘theoretical loss’ across the eight game types for the 100,000 players, we also found the bet size alone explained only 72% of the variance of the theoretical loss (not as large as we found in our simulation study but that was most likely because we had more games in the simulation study and the games in the simulation study were approximated house advantages whereas the follow-up study used actual house advantages.

This study broadly confirmed the findings from our previous simulation study. The results of our most recent study suggest that future research and particularly those that utilize behavioural tracking approaches should measure their participants’ gambling intensity by incorporating the game-specific theoretical loss instead of using proxy measures such the bet size and/or the amount of money staked. Another implication is that previously published research could be re-analysed using the more robust measure of gambling intensity presented here (i.e., theoretical loss) rather than the proxy measures that were used in the original published studies. This study demonstrates that bet size does not reliably indicate the amount of money that players are willing to risk as it does not take into account the house advantage of each individual game that gamblers engage in. The house advantage represents the percentage held back by the gaming operator and is essential for the amount lost in the long-term and will eventually be equal to the total losses that a player accumulates. In order to further generalize our results, further empirical research utilizing data from other online gaming platforms as well as land-based casino premises needs to be carried out.

Dr Mark Griffiths, Professor of Gambling Studies, International Gaming Research Unit, Nottingham Trent University, Nottingham, UK

Additional input: Michael Auer

Further reading

Auer, M. & Griffiths, M.D. (2013). An empirical investigation of theoretical loss and gambling intensity. Journal of Gambling Studies, in press.

Auer, M., Schneeberger, A., & Griffiths, M.D. (2012). Theoretical loss and gambling intensity: A simulation study. Gaming Law Review and Economics, 16, 269-273.

Broda, A., LaPlante, D. A., Nelson, S. E., LaBrie, R. A., Bosworth, L. B. & Shaffer, H. J. (2008). Virtual harm reduction efforts for Internet gambling: effects of deposit limits on actual Internet sports gambling behaviour. Harm Reduction Journal, 5, 27.

Colbert, G., Murray, D., Nieschwietz, R. (2009). The use of expected value in pricing judgements. Journal of Risk Research, 12, 199-208.

Griffiths, M.D. & Auer, M. (2011). Online versus offline gambling: Methodological considerations in empirical gambling research. Casino and Gaming International, 7(3), 45-48.

Griffiths, M.D. & Whitty, M.W. (2010). Online behavioural tracking in Internet gambling research: Ethical and methodological issues. International Journal of Internet Research Ethics, 3, 104-117.

LaBrie, R.A., Kaplan, S., LaPlante, D.A., Nelson, S.E., & Shaffer, H.J. (2008). Inside the virtual casino: A prospective longitudinal study of Internet casino gambling. European Journal of Public Health, 18, 410-416

LaPlante, D. A., Schumann, A., LaBrie, R. A., & Shaffer, H. J. (2008). Population trends in Internet sports gambling. Computers in Human Behavior, 24, 2399–2414.

Li, S. (2003). The role of Expected Value illustrated in decision-making under risk: Single-play vs multiple-play. Journal of Risk Research, 6, 113-124.

Wardle, H., Moody, A., Griffiths, M.D., Orford, J. & and Volberg, R. (2011). Defining the online gambler and patterns of behaviour integration: Evidence from the British Gambling Prevalence Survey 2010. International Gambling Studies, 11, 339-356.

Track to the future: Online behavioural tracking and problem gambling

Almost everyone reading this will be aware that problem gambling lies towards one end of a continuum that ranges from non-gambling at one end through to pathological gambling at the other. However, it should also be noted that there will always be some behaviours that are typically engaged in by problem gamblers that some non-problem gamblers may also engage in at least occasionally (e.g., chasing behaviour when gamblers try to recoup their losses).

Worldwide, there are many different screening instruments that can be used by clinicians and researchers to help identify problem gambling. One of most regularly used is the Diagnostic and Statistical Manual, Fourth Edition (of which the fifth edition has just been published) that includes criteria that can aid the diagnosis of problem and pathological gambling (but now called disordered gambling in its latest incarnation). The previous (DSM-IV) criteria were used in the most recent British Gambling Prevalence Survey published in 2011. If a person answered positively to at least five of the criteria, a diagnosis of pathological gambling would be made whereas endorsement of three or four of the criteria would indicate a diagnosis of problem gambling. Using the DSM-IV, the latest BGPS reported a problem gambling rate of 0.9% among British adults.

In contrast to offline gambling, the use of online behavioural tracking presents an opportunity for researchers to examine the actual and real-time behaviour engaged in by gamblers. Analysis of behavioural tracking data has been carried out by various groups of researchers. For instance, one group affiliated to Harvard University have published a series of papers examining a data set of online gamblers provided by the bwin gaming company. My own research unit has also been publishing data using behavioural tracking data provided by the win2day gaming company.

During my consultancy for various online gaming companies, I have been informed by industry insiders that problem gambling can be identified online by examining the patterns and behaviours of online gamblers. If this is true, it has implications for current problem gambling screens (including the new DSM-V). This is because most criteria found in these screens are associated with the consequences of problem gambling rather than the gambling behaviour itself. Take the DSM-IV. I have argued that only a few of the behaviours in the DSM criteria for pathological gambling can be reliably spotted online using online behavioural tracking (the most obvious being chasing losses, salience/preoccupation, and tolerance). The following list highlights each of the DSM-IV questions for pathological gambling and the component of pathological gambling that each criterion is assessing. This is followed by an assessment as to what extent each criterion can be identified online.

• Salience/Preoccupation (Do you find that you are becoming preoccupied with past gambling successes or find yourself spending increasingly more time planning future gambling?) – An online problem gambler is likely to spend a lot of time gambling online although this behaviour in itself does not necessarily indicate a problem. Anything above four hours daily play over a protracted period could be considered excessive although some forms of online gambling (e.g., online poker) may take up a lot of time and be played relatively inexpensively.
• Tolerance (Do you find that you need to increase the amount of money you gamble to achieve the same enjoyment and excitement?) – If experiencing tolerance to gambling, an online problem gambler is likely to have changed their gambling behaviour in one of two ways over time. The first example of tolerance is a gradual increase of daily play in terms of time. For instance, the gambler might start off playing 30-60 minutes a day but over the course of a few months starts to play increasing amounts of time. The second example of tolerance is the act of gambling using gradually bigger stakes over time. An online problem gambler is more likely to experience both of these combined (i.e., gambling for longer and longer periods of time with bigger and bigger amounts of money).
• Relapse (Have you recently tried to stop gambling but were unsuccessful?) – Although this is difficult to detect with absolute certainty online, a typical pattern would be a gambler who gambles heavily, day-in day-out, for a period of time and then “disappears” for a period of time (which could be days, weeks, and sometimes even months), only to suddenly re-appear and gamble heavily again.
• Withdrawal  (Do you become moody or impatient when you are cutting down how much you gamble?) – This is again difficult to detect with absolute certainty online but is most likely to surface with the use of verbally aggressive comments in those games that have chat room facilities (such as online poker).
• Escape from reality (Do you ever use gambling a way of ignoring stress in your in life or even pick you up when you feel down?) – This is almost impossible to detect online although those players who play for long hours every day are more likely to experience escape-like feeling.
• Chasing losses (Do you ever try to win back the money you lost by increasing the size or frequency of your wagers?) – This is one of the key indicators of problem gambling and can be spotted online more easily than many other problem gambling criteria. Typical chasing patterns will include repeated ‘double or quit’ strategies in an effort to recoup losses. Although many gamblers use this strategy on occasion, the online problem gambler will do it repeatedly. This behaviour, above and beyond any other criteria, is most likely to signal problem gambling.
• Conceal Involvement (Do you ever hide how much or how often you gamble from significant others?) – There is no way that an online gambling operator can spot this during online gambling unless such admissions are given to other players in online chat rooms.
• Unsociable Behaviour (Have you ever committed fraud or theft to get money to gamble with?) – Again, there is no way that an online gambling operator can spot this during online gambling unless such admissions are given to other players in online chat rooms.
• Ruin a Relationship/Opportunity (Has gambling ever ruined a personal relationship or an occupational or educational opportunity?) – As with the previous two criteria, there is no way that an online gambling operator can spot this during online gambling unless such admissions are given to other players in online chat rooms.
• Bail-out  (Have you ever needed others to relieve a financial problem created by gambling?) – When an online gambler has exhausted all their own funds, they will often ‘beg, borrow and (eventually) steal’ money to continue gambling. A player whose account is constantly ‘topped up’ by people other than themselves may be a problem gambler.

This brief analysis of the extent to which each DSM criterion of problem gambling can be identified online shows that only a few behaviours can be reliably spotted via online behavioural tracking. The following list contains a number of behaviours that are engaged in by online problem gamblers. This was devised and based on my conversations with members of online gaming industry. These are additional to those identified above (i.e., chasing losses, spending high amounts of time and money, and increasing the amount of gambling over time). As a general ‘rule of thumb’, it is assumed that the more of these online behaviours that are engaged in by an individual, the more likely that person is to be a problem gambler.

• Playing a variety of stakes – Playing a variety of different stakes (in games like online poker) indicates poor planning and may be a cue or precursor to chasing behaviour.
• Playing a variety of games – Evidence from national prevalence surveys (e.g. Wardle et, al, 2011) demonstrates that the more types of gambling engaged in, the more likely the person is to be a problem gambler. Although this factor on its own is unlikely to indicate problem gambling, when combined with other indicators on this list may be indicative of problem gambling.
• Player ‘reload’ within gambling session – Although any gambler can engage in such behaviour, players who deposit more money within session (‘reload’) are more likely to be problem gamblers. This indicates poor planning and is a cue to chasing behaviour.
• Frequent payment method changes – The constant changing of deposit payment methods indicates poor planning and is may be a cue to chasing behaviour. This online behaviour usually indicates shortage of funds and need to extract monies from a variety of sources. Such behaviour can also indicate bank refusal.
• Verbal aggression – Aggressive verbal interaction via relay chat is common among problem gamblers although any gambler losing money may cause such behaviour. Such behaviour may be evidence of gamblers going on ‘tilt’ (i.e., negative cognitive and emotional reaction to losing) or withdrawal effects if out of money to gamble.
• Constant complaints to customer services – Constant complaints to the customer service department is common among problem gamblers although any gambler losing money may cause such behaviour. As with verbal aggression, such behaviour may be evidence of gamblers going on ‘tilt’ (i.e., negative cognitive and emotional reaction to losing).

Clearly, each of these behaviours needs to be examined in relation to at least three or four other indicative behaviours. Perhaps most importantly, and according to online gambling companies who use socially responsible behavioural tracking tools, it is a significant change in usual online behaviour that is most indicative of a problem gambler. Most statistical modelling of player behaviour predicts future problematic behaviour on the basis of behavioural change over time. The behaviours highlighted suggest that screening instruments in the future may be able to be developed that concentrate on the gambling behaviour itself, rather than the associated negative consequences.

Dr Mark Griffiths, Professor of Gambling Studies, International Gaming Research Unit, Nottingham Trent University, Nottingham, UK

Further reading

Auer, M. & Griffiths, M.D. (2013). Limit setting and player choice in most intense online gamblers: An empirical study of online gambling behaviour. Journal of Gambling Studies, in press.

Auer, M. & Griffiths, M.D. (2013). An empirical investigation of theoretical loss and gambling intensity. Journal of Gambling Studies, in press.

Delfabbro, P.H., King, D.L & Griffiths, M.D. (2012). Behavioural profiling of problem gamblers: A critical review. International Gambling Studies, 12, 349-366.

Dragicevic, S., Tsogas, G., & Kudic, A. (2011). Analysis of casino online gambling data in relation to behavioural risk markers for high-risk gambling and player protection. International Gambling Studies, 11, 377–391.

Griffiths, M.D. (2009). Social responsibility in gambling: The implications of real-time behavioural tracking. Casino and Gaming International, 5(3), 99-104.

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. & Whitty, M.W. (2010). Online behavioural tracking in Internet gambling research: Ethical and methodological issues. International Journal of Internet Research Ethics, 3, 104-117.

LaBrie, R.A., Kaplan, S., LaPlante, D.A., Nelson, S.E., & Shaffer, H.J. (2008). Inside the virtual casino: A prospective longitudinal study of Internet casino gambling. European Journal of Public Health, DOI:10.1093/eurpub/ckn021.

LaPlante, D.A., Kleschinsky, J.H., LaBrie, R.A., Nelson, S.E. & Shaffer, H.J. (2009). Sitting at the virtual poker table: A prospective epidemiological study of actual Internet poker gambling behavior. Computers in Human Behavior 25, 711-717.

Wardle, H., Moody, A., Spence, S., Orford, J., Volberg, R., Jotangia, D., Griffiths, M., Hussey, D. & Dobbie, F. (2011). British Gambling Prevalence Survey 2010. London: The Stationery Office.

Identity floored: Can gambling addicts be identified in gambling venues?

Although the behavioural characteristics of problem gamblers have been studied for several decades, it has only been the in last decade that there has been interest in studying gambling within the gambling venue itself. Along with my research colleagues Dr. Paul Delfabbro and Dr. Daniel King at the University of Adelaide, we have just published a paper in the journal International Gambling Studies reviewing all the studies that have examined whether problem gamblers and gambling addicts can be identified as having problems based on their gambling within gambling environments.

For instance, a 2004 study published in the journal Gambling Research by Dr. Tony Schellinck and Dr. Tracy Schrans obtained data from a population sample of 927 video lottery terminal (VLT) gamblers in Nova Scotia (Canada) of whom 16.5% were problem gamblers (as measured by the Problem Gambling Severity Index. Based on their analyses, the authors found that the most common experiences or behaviours reported by problem gamblers in terms of frequency were spending three-quarters of their time gambling, gambling for more than 180 minutes in one session, feeling angry, and sweating. Feeling sick or sad or gambling for over 180 minutes in one session were the factors that most strongly differentiated problem gamblers from other gamblers. For example, a person was around three times more likely to be a problem gambler as compared with the base-rate in the sample if they reported feeling sick while gambling. Some indicators (using credit cards, shaking, going out to get cash) were more commonly reported by problem gamblers, but did not occur very often when problem gamblers played VLTs.

A Swiss study carried out by Dr. Jorg Hafeli and Dr. Caroline Schneider in 2006 carried out qualitative interviews with a sample of 28 problem gamblers, 23 casino employees and seven regular gambling patrons in an attempt to develop a range of indicators that might be used to identify problem gamblers within Swiss casinos. Material from these interviews was content analysed and classified into meaningful categories. Only statements that were simple and concise, and which referred to concrete examples of behaviour were included. Problem gamblers were perceived as those who gambled more intensely and frequently, who were compelled to find many different ways to raise funds to defray the costs of gambling, and whose social and emotional responses differed from other gamblers. Problem gamblers were seen to be more socially withdrawn, angry, anxious, depressed, but also more immersed in the activity. Most of these items appeared to have good face validity as indicators of problem gambling.

A similar Australian study undertaken by Dr. Paul Delfabbro and his colleagues in 2007, but which also drew upon material from the two studies outlined above. Unlike the previous studies, attempts were made to develop indicators that were not so specifically focused on particular activities (e.g., casino table games), but which could be applied to venue-based gambling more broadly. Once again, there were items that referred to the statistically unusual frequency or intensity of gambling; evidence concerning gamblers’ need for funding while gambling; variations in social and emotional responses, but also evidence that gamblers had lost control over their gambling urges.

In an initial stage of this research, a list of indicators was provided to both venue staff (n=120) and counsellors (n=20) recruited from several different parts of Australia. Both groups of respondents were asked to indicate whether each item in the checklist was a valid indicator of problem gambling. The main component of the research was a detailed survey of almost 700 regular gamblers recruited either from the general community or from outside gaming venues. Participants were eligible to participate if they gambled at least fortnightly on electronic gaming machines, casino games or sports and race betting, although the principal focus was on gaming because this is largely venue-based. All respondents completed the Problem Gambling Severity Index with 20% classified as problem gamblers.

Their analyses were based on the proportion of problem and non-problem gamblers who reported producing the particular behaviour rarely or more often. There was one group of indicators that occurred relatively quite commonly in problem gamblers, but which were also reported by a moderate proportion of other regular gamblers. A second group were more rarely reported, but typically only by problem gamblers. Some activities, such as using ATMs on several occasions, playing very fast, or try very hard to win on one machine were relatively common amongst problem gamblers (similar to observations reported by an observational study I carried out way back in 1991 and published in the Journal of Community and Applied Social Psychology in his longitudinal study of British amusement arcade players), but also reported by a modest proportion of other gamblers. By contrast, very strong emotional responses or attempts to disguise one’s gambling were rarely reported by non-problem gamblers. The strongest predictors for males appeared to relate to impaired control (i.e., an inability to stop gambling) and emotional responses, whereas strong emotional responses and a preoccupation with gambling appeared most indicative when considering female problem gamblers.

Although these studies found theoretical support for the notion that there are valid indicators available to identify problem gamblers in venues, there are a number of caveats that need to be applied to these findings. The first difficulty is that all of the studies described involved only single samples. For models to be usefully applied to support harm minimisation policies, it would be important to show that models developed in one sample can be replicated using another. A second difficulty is that survey-based responses do not provide a lot of information concerning the practical reality of observing and consolidating information in a venue environment. Even if the same staff members are available in the venue over a protracted period, it does not necessarily follow that they will have the ability to observe the same patrons all the time.

Another potential challenge for the identification process is that studies are based on aggregate results. Although problem gamblers are likely to share many similarities, it is also known that different subgroups of gamblers very likely exist. These views suggest that the significance of particular indicators may, therefore, differ depending upon the type of gambler. For example, in a number of these models or typologies, a distinction is often drawn between gamblers who are emotionally vulnerable and gamble to escape from feelings of anxiety or depression and those who gamble because of the excitement or ‘action’. Those gamblers who are more emotionally vulnerable may be more likely to display emotion when they gamble and be detectable because of these characteristics, whereas there may be others whose behaviour is distinctive because of stronger externalised behaviours (e.g., displays of anger, large bet sizes, histrionics, etc.). At present, based on existing research evidence, it is difficult to determine whether visible indicators cluster according to these subtype models, but it will be important for this possibility to be considered in future research.

Dr Mark Griffiths, Professor of Gambling Studies, International Gaming Research Unit, Nottingham Trent University, Nottingham, UK

Additional contributions from Dr. Paul Delfabbro and Dr. Daniel King

Further reading

Delfabbro, P.H., Osborn, A., McMillen, J., Neville, M., & Skelt, L. (2007). The identification of problem gamblers within gaming venues: Final report. Melbourne, Victorian Department of Justice.

Delfabbro, P.H., Borgas, M., & King, D. (2011). Venue staff knowledge of their patrons’ gambling and problem gambling. Journal of Gambling Studies, 27, 1-15.

Delfabbro, P.H., King, D.L & Griffiths, M.D. (2012). Behavioural profiling of problem gamblers: A critical review. International Gambling Studies, 12, 349-366.

Ferris, J. & Wynne, H. (2001). The Canadian Problem Gambling Index Final Report. Phase II final report to the Canadian Interprovincial Task Force on Problem Gambling.

Griffiths, M.D. (1991). The observational study of adolescent gambling in UK amusement arcades. Journal of Community and Applied Social Psychology, 1, 309-320.

Hafeli, J. & Schneider, C. (2006). The early detection of problem gamblers in casinos: A new screening instrument. Paper presented at the Asian Pacific Gambling Conference, Hong Kong.

Schellinck, T., & Schrans, T. (2004). Identifying problem gamblers at the gambling venue: Finding combinations of high confidence indicators. Gambling Research, 16, 8-24.