1. Preparing for the future: Emerging forms of gambling and new technologies
Jeffrey L. Derevensky, Ph.D.
James McGill Professor, School/Applied Child Psychology
Professor, Psychiatry
McGill University
Research Director,
Florida Council on Compulsive Gambling
National Council of Legislators from Gaming States
Denver, June, 2017

2. The normalization of gambling/gaming
Transition to Jeff

4. Chocolate poker chips for kids Poker chips for adults

11. Arcades

20. Safety 1st Jack Potty Training Seat

21. Gambling (gaming) has become glamorized

25. Ryan Reiss, age 23, wins $8.36 million in 2013 WSP recent college grad

26. The new face of youth gambling

29. Youth involvement in addictive behaviors

30. Involvement in addictive behaviors
Total use Weekly use
Gr 7 Gr 9 Gr 11 Gr 7 Gr 9 Gr 11
Alcohol 36.8% 62.2% 79.8% 7.4% 14.0% 20.2%
Drugs % 13.4% 26.5% 2.7% 2.1% 9.0%
Cigarettes 18.2% 34.5% 48.4% 7.0% 16.1% 31.4%
Gambling 79.1% 78.9% 83.4% 30.4% 37.4% 37.1%

31. Prevalence of problem gambling 1-2% of adults 4-6% of adolescents

32. Emerging Forms of Gambling: Concerns?

33. Circa 1920’s Slot Machine

34. Slot Machine 2013

37. Skill-Based Slots

38. Guitar Warrior

39. Texas Tea Pinball

41. The millennials
Transition to Jeff

43. Internet gambling

45. Are Internet gamblers more likely to have problems?
Wood & Williams (2007) - Sample of on-line adult gamblers
Non-problem gamblers: 34%
At-risk gamblers: 24%
Moderate problem gamblers: 23%
Severe problem gamblers: 20%
2/3 of those respondents gambling on the Internet are likely to have problems
2001 = upwards of 1400 different online gambling sites
Recent research suggest I-gamblers, compared to non-I gamblers, are more likely to be suffering from a gambling problem and are at greater risk of developing one (Azmier, 2000; Hammer, 2001)
Unclear whether I-gambling leads to problems, or problem gamblers are led to Internet
Experience may facilitate emergence of gambling problem: convenience, ease of accessibility, immersive, anonymous
Visual, aural, tactile qualities of interface may accelerate passage of time; easy access from home may lead to playing more often; psychological value of electronic cash – may result in greater than normal gambling losses
To what extent do I-gamblers show tendency for problem gambling (CPGI)? What are correlates and predictors of problem gambling among I-gamblers?
N=1920, 56% men, avg age=34 (range=18-84), 87% from U.S.
Internet users as well as gamblers: , chat, IM, banking, shopping
Gambling mean=5 hours/week
Predictors of problem gambling status: time spent gambling, East Asian, South Asian, African ancestry, preference for non-Internet gambling (maybe problem gamblers go to Internet as one more place to gamble), male gender
They suggest the rate of problem gambling among Internet gamblers may be 10x higher than the rate among the general population

46. Mobile wagering

49. Social casino gambling

50. Minneapolis Airport

51. Social Casino Gamers
There are currently 170 million social casino gamers, well over triple the number of online gamblers (Morgan Stanley, 2012).
The Morgan Stanley Blue Paper (Morgan Stanley, 2012) highlighted the importance of trying to migrate social casino gamblers to become online gamblers,"

52. U.K. Gambling Commission (2015)
The boundaries between social gaming and commercial gambling have become increasingly blurred and are of concern for young people as a result of:
the growth in use of social media for social gaming and gambling
an increasing convergence between the products of traditional gambling and social gaming businesses
significant investment  by companies developing new products or ways of marketing existing products

53. Expansion of sports wagering

54. Sports Wagering in the U.S.
Recent poll: 65% of fans support regulated sports wagering (AGA, 2016)
A Nov 1, 2016 Fairleigh Dickinson University Public Mind poll found 48% support changing federal law to make sports wagering legal throughout U.S. (39% opposed)
Among those in favor 45% are already doing it; 39% view increased state revenues
Among those opposed 55% are concerned about increased gambling addiction; 22% concerned about organized crime; 16% concerned about the integrity of the game

55. Prevalence of Sports Wagering by Gender among Adolescents Ages 12 to 18 in Wood County, Ohio (n=5183)
Gender
Daily
About once a week
About once a month
Less than once a month
Total
Bet money on sports teams (pro, college, or amateur)
Female
Male .3
1.8
3.0
1.4
5.4
5.0
9.8 7%
20%
Bet money on fantasy sports or games (with an entry fee)
1.9 .1
2.6 .2
2.7
1.7
4.9
2.3%
12.1%
Bet or wager on daily fantasy sports (FanDuel or DraftKings, etc.)
2.2
2.3 .9
2.4
1.4%
8.7%

56. Participation in Fantasy Sports by Gambling Severity (2012)
Male
Female
Social Gamblers
At-Risk/PPG
Free Fantasy Leagues
52.2%
65.4%
8.5%
44.4%
Fee-Based Fantasy Leagues
18.4%
48.1%
1.8%
25.0%
Marchica & Derevensky (2015)

57. Parental perceptions: Serious Youth Issues

58. Harm minimization: Some approaches
School-based education prevention initiatives
Limit setting and use of smart card technology
Responsible gambling centers within casinos
Algorithms to identify high risk players
Normative feedback and messaging
Advertising standards

59. mentor Early Risk Detection Harm Minimization Player Communication
Self-Assessment

60. Tailored Recommendation
The right recommendation for the right player at the right point of time

61. PRESENTATION RIGHTS RESERVED. COPYRIGHT BETBUDDY LTD. YEAR 2017.
Assessment
Interpretation
Interaction
Evaluation
1. Assessment
2. Interpretation
Based on research, operators build an evidence base using player data and apply quantitative and qualitative methods to classify players on a continuum of risk
Transparent RG models enable solutions to be independently tested by experts and regulators and enable operators to understand each player’s risk profile
PRESENTATION RIGHTS RESERVED. COPYRIGHT BETBUDDY LTD. YEAR 2017.

62. 3. Interaction 4. Evaluation
Assessment
Interpretation
Interaction
Evaluation
3. Interaction
4. Evaluation
Message Type
General, Personal, Normative, etc.
Messaging Channel
, Telephone, SMS, In-Game, etc.
Messaging Strategy
Single, Multiple, Ad-hoc, Regular, etc.
Marketing Strategy
Promote RG marketing to high risk, etc.
Data shows RG Interactions resulted in 24% of regular players, who exhibited some risk, significantly moderating behaviours
The RG data insights generated can be used to drive targeted responsible gambling interactions with the customer base to help players to make informed choices
Operators have the opportunity to develop trials to assess optimal player interaction approaches via continuous experimentation and testing
PRESENTATION RIGHTS RESERVED. COPYRIGHT BETBUDDY LTD. YEAR 2017.