Early Detection Items and Responsible Gambling Features for Online - - PowerPoint PPT Presentation

Lucerne

• Prof. Jörg Häfeli, Projectmanager
• lic. rer. soc. Suzanne Lischer, Research Associate

T direct +41 41 367 48 47 joerg.haefeli@hslu.ch

Early Detection Items and Responsible Gambling Features for Online Gambling

EASG-Conference, Vienna, 14 – 17 September 2010

Slide 2

Acknowledgments This research was supported by a grant from the European Gaming and Betting Association. Funding bodies had no influence over design and conduct of the study, and analysis and interpretation of the data. We would like to thank for interviews and data (in alphabetical order): bwin Interactive Entertainment AG PartyGaming Plc Unibet Group Plc

Slide 3

The specific risks of Internet Gambling Not long after gambling was brought to the Internet, first assumptions about the addictive nature of the new medium were published. Griffiths (1999):

• High availability (everywhere) and accessibility (24/7)
• Lacking social protection (underage gambling or gambling while intoxicated)
• Usage of electronic cash
• Risk of fraud

These concerns are often been repeated or slightly modified manifold (Hayer & Meyer, 2004; Griffiths et al., 2006; Williams, West & Simpson, 2007; Wood & Williams, 2007). Still there was a remarkable lack of empirical evidence: Until 2007 there is no published research, based on actual internet gambling behavior (c.f. Peller et al., 2008).

Slide 4

Actual Internet Gambling Behavior Evaluation of actual Internet Gambling behaviour showed, that despite the speculated risks, the gambling behaviour of the vast majority is very moderate (LaBrie et al., 2007; LaBrie et al., 2008; LaPlante et al.; 2009). As well in population based prevalence studies no increased risk for Internet gambling could be found (Welte et al., 2009; LaPlante et al., 2010). Considering the epidemiological triangle, potential explanations for these effects could lie in protective properties only the technology of the Internet can offer for the time being:

• Pre-commitment methods (Nelson et al., 2008)
• Higher transparency of losses (Productivity Commission, 2010)
• Different (earlier) usage of responsible gaming tools (Meyer & Hayer, 2010)

Slide 5

RG Measures in land-based and Internet Gambling

Protective measures for gamers Land-based Gambling Internet Gambling Exclusion Partial exclusion from single types of games not possible common practice Self-exclusion common practice common practice Prescribed exclusion common practice common practice Pre-Commitment / Limitation Limit to gaming volume not possible (except for a minority applying smart-cards) common practice Limit to gaming time not possible (except for a minority applying smart-cards) possible Limit to gaming frequency possible possible Transparency Succinct presentation of the gaming time possible (restricted to the time spent on a single EGM) common practice Succinct presentation of the gaming volume not possible common practice Succinct presentation of the gaming frequency possible common practice Information offering Awareness material and responsible gaming advice common practice common practice Self tests possible common practice Interactive Self-help / eHealth tools not possible possible Contact with qualified healthcare structure common practice common practice Under-age protection Access limitations possible (but with many forms of land-based gambling not implemented) common practice Handling credit No award of credit common practice common practice

Slide 6

Identification of at-risk and Problem Gamblers In the light of an individualized protection of gamblers (c.f. Blasczcynski, Ladouceur & Shaffer, 2004) early detection of at-risk and problem gamblers is a central requirement for any responsible gaming framework.

Land-based Gambling Internet Gambling Observation of gambling behavior

Typically not observable in an

• bjective way

(comprehensive usage of smart- cards may offer first approaches) Stored and readily availiable for longitudinal analysis (Xuan & Shaffer, 2009; Braverman & Shaffer, 2010)

Observation of social interaction behavior

Availiable – quality of observations depends on standardiation of protocols and training of staff (Allcock, 2002; Schellinck & Schrans, 2004; Hafeli & Schneider, 2005)

?

Slide 7 7, 30 September 2010

Social behavior as a predictor of gambling-related problems in land- based gambling (assortment) Allcock (2002) Schellinck & Schrans (2004) Häfeli & Schneider (2005) Delfabbro et al. (2007) Repeated visits to an ATM; borrowing money

• n sites; trying to

cash cheques; disorderly behaviour; family enquires; long sessions, etc. Nausea; depression; headaches; gambler plays longer that 3 hours; borrowing money; shaking; feeling edgy; etc. Frequency of visits; duration of visits; guest borrows money from other guests; level of bets per visit; guest gambles almost uninterruptedly, etc. Gamblers plays longer than 3 hours; loose track

• f what is going
• n around them,

play quickly without a proper break; favour gaming machines; etc.

Slide 8 8, 30 September 2010

Goals of the project Considering the fact, that the lack of social interaction was typically named

• ne of the risks of Internet gambling, one would expect the analysis

thereof will not be feasible in the Internet. However, online gambling operators communicate with their customers as well; typically via email or telephone - amounting up to 150,000 customer contacts per month per operator. The aim of this study is to generate a basic theoretical and empirical guideline which permits the development, implementation and validation of objective protocols for early detection of gambling issues based on customer communication behavior.

Slide 9

Study I - Semi-structured interviews with senior customer-services staff Sample:

• 8 senior staff members from 3 private internet gambling operators
• Interview duration between 45 and 60 minutes

Learnings:

• Customer communication does contain indicators for future gambling

problems

• Cannot be solely based on discrete key-words; the problem is defined by

the context Risk Indicators identified:

• Chasing losses
• Financial situation / financial requests
• Loss of control
• Family or social situation
• Heavy complaining / allegation of fraud
• Criminal Activities / threats
• Health issues

Slide 10

Study II – Prospective Analysis of Customer Communication The second part of the study should be understood as a confirmatory investigation with the goal to investigate how far the indicators, identified in the previous study are able to predict manifest gambling related problems in a prospective empirical design. Criterion Definition: As a problem criterion, gamblers were selected who excluded themselves from gambling because of gambling-related issues. Customers who closed their account for any other reasons (e.g. not satisfied) were not selected. Sample: 150 randomized self-excluders; 150 randomized controls

Independent of the type of game due to feasibility reduced to customers communicating in English or German

All communication of both groups was analyzed: 1008 mails (observer-blinded design, 2 independent raters) Inter-rater reliability: 0.78

Slide 11

Hypothetizised risk-indicators

Content-based Indicators:

• Chasing losses
• Financial problems
• Loss of control
• Social situation
• Criminal acts
• Health issues
• Doubts about results of games/bets
• Request for an increase of betting limits
• Request for lower limits
• Request for partial blocking
• Request for account reopening
• Technical problems
• Account administration
• Financial transaction
• Request for bonus
• Announcement / threat of account closure

Tonality-based Indicators:

• Complaining
• Threatening

Other Indicators:

• Frequency of customer contacts
• Immediate repeats

Slide 12

Description of the Sample

Self- Excluders Controls Gender 94.6% Men 91.3% Men Age 31.5 32.9 Self- Excluders Controls Communi- cation available 52.7% 39.3%

• Nr. of mails

8.3 3.3

Socio-demographics: Communication While Self-Excluders and random controls do not differ in their socio- demographics, they do differ in their communication behavior.

Slide 13

Density of communication in relation to the date of self-exclusion 43% of all communication of self-excluders happens during the final 6 months prior to self-exclusion.

Slide 14

Descriptives: Content Predictor Frequencies

Predictor Frequency Chasing losses 0 % Financial problems 0 % Loss of control 0 % Social situation 0 % Criminal acts 0 % Health issues 0 % Results of games / bets 25.5% Increase limits 2.0% Predictor Frequency Lower limits 0.2 % Partial blocking 0 % Account reopening 6.0 % Account administration 13.5 % Technical problems 3.6 % Financial transaction 34.3 % Request for bonus 4.8 % Threat of account closure 0.6 %

Slide 15

Descriptives: Tonality Predictor Frequencies

Predictor Frequency Neutral 58.0% Complaining 36.7% Threatening 5.3%

Slide 16

Bivariate Analysis: Spearman Correlations

Predictor r Results of games / bets 0.194** Increase limits 0.096 Lower Limits 0.058 Account reopening 0.232** Account administration 0.180** Technical problems 0.150** Financial transaction 0.252** Request for bonus 0.037 Threat of account closure 0.130* Predictor r Complaining 0.268** Threatening 0.239** Predictor r Age

• 0.081

Frequency 0.442** Immediate repeats 0.405**

Several predictors do correlate moderately with the criterion. Especially the frequency of customer contacts seems to be an important indicator.

* significant at p=0.05 ** significant at p=0.01

Slide 17

Multivariate Prediction Optimal Prediction rate was achieved by a logistic model consisting of:

• Frequency of Customer Contact

Relative share of communication topics:

• Account reopening
• Account administration
• Financial transactions

Tonality:

• Threatening tonality

Slide 18

Multivariate Prediction Validity of the model: R = 0.57 Classification Rate: 76.6%

Slide 19

Discussion I Customer communication does indeed contain information about whether or not gamblers are at-risk of developing gambling related problems. However problems manifest primarily indirectly over high emotional involvement & distress, heavy complaining and failure to cope with arising problems.



Repeated closing / reopening of the account (for any reason) & frequent administrative changes in the gambler‘s account



Harsh tonality



Extensive communication about financial issues (credit card change, not having received the withdrawal yet,...)

Slide 20

Discussion II Analysis of customer communication is possible for online gambling too. It appears to yield powerful indicators for at-risk gambling, which is – as typical for the internet – easily accessible and open for a rule-based objective evaluation. Nevertheless communication behavior should not be the sole source of information, but instead be combined with other objective methods of behavioral analysis. Detection of at-risk gamblers based on customer communication is only possible for those who did communicate. However there seems to be a sub- group of at-risk gamblers that does not communicate at all. In order to detect this group, other variables (e.g. objective gambling behaviour) need to be analyzed.

Slide 21

Evaluation of practical applicability (in a realisitic sample) Feasible Risk Compromise: Sensitivity: 53.7% Specificity: 91.3% Trying to achieve higher levels of sensitivity, will inevitably lead to a higher amount of false-positive detections.

Slide 22

Evaluation of practical applicability (in a realisitic sample) Moderate gamblers At-risk gamblers No Communication 57.7% 2.4% Communication 37.3% 2.6% Total 95% 5% Not Detected  Users without communication 57.7% moderate gamblers 2.4% at-risk gamblers  Users with communication and negative detection 34.1% moderate gambler 1.2% at-risk gamblers Detected  Users with communication and positive detection 3.2% moderate gamblers 1.4% at-risk gamblers

Slide 23

Model Total Evaluation Moderate gamblers At-risk gamblers not detected 91.8% 3.6% detected 3.2% 1.4% Classification Rate: 93.2 % Sensitivity: 28.0 % Specificity: 96.6 % While being able to detect roughly one third of all potential problem gamblers solely based on the analysis of correspondence, the impact of moderate gamblers falsely assumed to be at-risk is minimal. The model would produce a rate of 93.2% correct classifications in practical application.

Slide 24

Take-home messages for the Industry

• Alongside with the analysis of objective gambling behaviour, customer

communication analysis should be used as a further powerful method of identifying at-risk gamblers.

• In order to generalize these results over different online gambling operators

with different products and different customer segments, replication studies should be initiated.

• Industry should not expect to be able to identify at-risk gamblers based on

manifestation of DSM-IV criteria. Instead, looking for “difficult customers“ might be a more viable approach.

• Results underline the importance of having dedicated, well-trained staff in

Customer Services, handling any suspicious communication.

• Industry should elaborate standardized, objective protocols for identification

and handling of at-risk gamblers based on customer communication.

Slide 25

Thank you very much.

Slide 26

Ressources

Allcock, C. (2002). Current issues related to identifying the problem gambler in the gaming venue. Australian Gaming Council: Current issues. Australian Gaming Council, Melbourne. Blaszczynski, A.; Ladouceur, R; Shaffer, H. J. (2004). A Science-Based Framework for Responsible Gambling: The Reno

• Model. Journal of Gambling Studies, 20, 301-317.

Braverman, J.; & Shaffer, H. J. (2010). How do gamblers start gambling: identifying behaviour markers for high-risk internet gambling. European Journal of Public Health. Delfabbro, P.; Osborn, A.; Nevile, M.; Skelt, L.; & McMillen, J. (2007). Identifying Problem gamblers in gambling venues. Gambling Research Australia, Melbourne. European Commitee for Standardisation (2010). Draft CWA – Responsible Remote Gambling Measures. CEN: Brussels. http://www.cen.eu/cen/Sectors/TechnicalCommitteesWorkshops/Workshops/Pages/WS58eGambling.aspx Griffiths, M. (1999). Gambling Technologies: Prospects for Problem Gambling. Journal of Gambling Studies, 15, 265-283. Griffiths, M.; Parke, A.; Wood, R.; & Parke, J. (2006). Internet gambling: An overview of psychosocial impacts. UNLV Gambling Research & Review Journal, 10, 13. Häfeli, J.; & Schneider, C. (2005). Identifikation von Problemspielern im Kasino – ein Screeninginstrument (ID-PS). Luzern. Hayer, T.; & Meyer, G. (2004). Sportwetten im Internet – Eine Herausforderung für suchtpräventive Handlungsstrategien. Suchtmagazin, 1, 33-41. LaBrie, R. A.; LaPlante, D. A.; Nelson, S. E.; Schumann, A.; & Shaffer, H. J. (2007). Assessing the Playing Field: A Prospective Longitudinal Study of Internet Sports Gambling Behavior. Journal of Gambling Studies, 23,347-363. LaBrie R. A.; Kaplan, S. A.; LaPlante, D. A.; Nelson, S. E.; & Shaffer, H. J. (2008). Inside the virtual casino: A prospective longitudinal study of actual Internet casino gambling. European Journal of Public Health, 18, 410-416. 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. LaPlante, D. A.; Nelson, S. E.; LaBrie, R. A.; & Shaffer, H. J. (2009). Disordered gambling, type of gambling and gambling involvement in the British Gambling Prevalence Survey 2007. European Journal of Public Health.

Slide 27

Ressources

Meyer, G.; & Hayer, T. (2010). Die Effektivität der Spielsperre als Maßnahme des Spielerschutzes. Frankfurt: Lang. Nelson, S. E.; LaPlante, D. A.; Peller, A. J.; Schumann, A.; LaBrie, R. A.; & Shaffer, H. J. (2008). Real limits in the virtual world: Self-limiting behavior of Internet gamblers. Journal of Gambling Studies, 24, 463-477. Peller, A. J.; LaPlante, D. A.; & Shaffer, H. J. (2008). Parameters for Safer Gambling Behavior: Examining the Empirical

• Research. Journal of Gambling Studies, 24, 519-534.

Productivity Commission (2010). Gambling, Report Nr. 50. Canberra. Schellinck, T.; & Schrans, T. (2004). Identifying Problem Gamblers at the Gambling Venue: Finding Combinations of High Confidence Indicators. Gambling Research: Journal of the National Association for Gambling Studies (Australia); 16, 8- 24. Welte, J. W.; Barnes, G. M.; Tidwell, M. C.; & Hoffman, J. H. (2009). The association of form of gambling with problem gambling among American youth. Psychology of Addictive Behavior, 23, 105-112. Williams, R. J.; West, B. L.; & Simpson, R. I. (2007). Prevention of Problem Gambling: A Comprehensive Review of the

• Evidence. Report

prepared for the Ontario Problem Gambling Research Centre, Guelph, Ontario, CANADA. Wood, R.; & Williams, R. J. (2007). Problem gambling on the Internet: Implications for Internet gambling policy in North

• America. New Media & Society, 9, 520-542.

Xuan, Z.; & Shaffer, H. J. (2009). How Do Gamblers End Gambling: Longitudinal Analysis of Internet Gambling Behaviors Prior to Account Closure Due to Gambling Related Problems. Journal of Gambling Studies, 25, 239-252.