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Giuseppe A. Veltri, PhD
Study on
measures for the protection of consumers of
gambling services
PAF Responsible Gaming Summ...
Background
Background on work for the Commission
• 2011 Call for a framework contract on behavioural studies
• LSE and Partners Conso...
System 1 (fast) System 2 (slow)
Quick, automatic, no
effort, no sense of
voluntary control
Continuous construal of
what is...
Objectives and experiments
Measure the
relative
effectiveness of
existing and
possible new
consumer
protective measures
La...
Design
LAB
ONLINE
Demographics
and gambling
experience
pre-treatment
questionnaire
RANDOM
1 out of 8 Treatments
Post-treatment
Questi...
Samples
• Laboratory experiment conducted at LSE Lab:
– Convenience sample (N= 522) extracted from LSE Behavioural Lab Pan...
Multi-dimensional response variables
Scales measuring:
• Emotional:
• Valence: type
• Arousal: intensity
• Fear / Anxiety
...
Key ‘nudges’ we tested
Nudges Rationale Possibly de-biasing
Pictorial warning
Elicitation of emotions;
activate reflective...
Laboratory experiment findings
Notation
Pre-gamble: behavioural measures
Pre-gamble: self-reported measures (PANAS)
Pre-gamble: self-reported measures (SAM)
In-gamble: behavioural measures
In-gamble: self-reported measures (PANAS)
Online experiment findings
Pre-gamble: behavioural measures
Conclusions
① Pre-gamble treatments can be deemed to be systematically not effective
– At least three of the pre-gamble tr...
g.a.veltri@le.ac.uk
@gaveltri
Paf Responsible Gaming Summit 2015: Responsible gaming from an international perspective. Giuseppe A. Veltri, Lecturer in ...
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Paf Responsible Gaming Summit 2015: Responsible gaming from an international perspective. Giuseppe A. Veltri, Lecturer in Social Psychology of Communication, University of Leicester, UK

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Giuseppe is Lecturer in Psychology of Communication at Media and Communication Department of the University of Leicester. He is also an associated researcher of UOC ASSBE research group. Giuseppe has worked as junior scientist at the European Commission Joint Research Centre IPTS (Seville) between 2008 and 2011 where he contributed to set up the IPTS behavioural economics studies research group. His research interests focus on public opinion research, social representations, social network analysis, behavioural economics and social psychology of economic life. Giuseppe has contributed to designing the experiments for the four behavioural studies realised by the LSE & Partners consortium in European Commission Framework on behavioural research for consumer protection: “Study on Tobacco Labelling and Packaging”; “Testing of different approaches to CO2/Car labelling and the effectiveness of mandatory consumer information in promotional material”; “Study on the impact of marketing through social media, online games and mobile applications on children´s behaviour”; “Study on online gambling and adequate measures for the protection of consumers of gambling services”.

The purpose of Paf Responsible Gaming Summit is to increase awareness about responsible gaming by inviting the gaming industry and its stakeholders. We wish to have an open dialogue with gambling operators, partners, academics and decision makers to learn from each other and share best practices https://www.paf.com/rgsummit2015

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Paf Responsible Gaming Summit 2015: Responsible gaming from an international perspective. Giuseppe A. Veltri, Lecturer in Social Psychology of Communication, University of Leicester, UK

  1. 1. Giuseppe A. Veltri, PhD Study on measures for the protection of consumers of gambling services PAF Responsible Gaming Summit 29TH SEPTEMBER 2015
  2. 2. Background
  3. 3. Background on work for the Commission • 2011 Call for a framework contract on behavioural studies • LSE and Partners Consortium among the five selected contractors • So far: – Study on tobacco labelling and packaging (two studies) – Study on Car labelling (C02 labels) – Study on online gambling and consumer protective measures – Study on online marketing and in app purchase for kids online – Study on environmental footprint labels
  4. 4. System 1 (fast) System 2 (slow) Quick, automatic, no effort, no sense of voluntary control Continuous construal of what is going on at any instant Slow, effortful, attention to mental activities requiring it Good at cost/benefit analysis, but lazy and saddled by decision paralysis (cognitive overload) Characteristics • Quick (Reflexive) • Heuristic based • Use shortcuts Characteristics • Deliberate (Reflective) • Conscious • Rule-based When it plays • When speed is critical • Avoid decision paralysis • When System 2 is lazy or not activated (not worth, no energy, lack of awareness) When it plays • May take over when System 1 cannot process data • May correct/override System 1 if effort shows that intuition or impulse is wrong By David Plunkert in NYT 27/11/2011 Thinking fast and slow
  5. 5. Objectives and experiments Measure the relative effectiveness of existing and possible new consumer protective measures Laboratory experiment (UK, N=522) Test subjects behavioural and self-reported responses to pre-gamble and in-gamble treatments Online experiment (7 countries, N=5997) Test subjects behavioural and self-reported responses to, and their choices with respect to, pre-gamble treatments
  6. 6. Design
  7. 7. LAB
  8. 8. ONLINE Demographics and gambling experience pre-treatment questionnaire RANDOM 1 out of 8 Treatments Post-treatment Questions 1 Gamble (≈ 20-30 spins) Online panel (N=5997) Filler task Post-treatment Questions 2 Post-treatment questionnaire Opt-out choice
  9. 9. Samples • Laboratory experiment conducted at LSE Lab: – Convenience sample (N= 522) extracted from LSE Behavioural Lab Panel – The LSE Behavioural Lab Panel comprises some 3000 contacts, who have expressed an interest in participating in paid research. The pool of subjects consists primarily of LSE students and staff, but also of individuals from surrounding universities • Online Experiment: – Simple random sample (N=5997), with sampling error +/- 1.25% for overall data and +3.53% for country-specific data
  10. 10. Multi-dimensional response variables Scales measuring: • Emotional: • Valence: type • Arousal: intensity • Fear / Anxiety Scales measuring: • Recall of info • Cognitive depth of processing Behavioural measures • Time per bet • Amount per bet • Opt-in or opt-out • Continue betting when experiment could be finished Scale measuring: • Future intentions
  11. 11. Key ‘nudges’ we tested Nudges Rationale Possibly de-biasing Pictorial warning Elicitation of emotions; activate reflective thinking Gambler’s fallacy, near-miss fallacy Overconfidence task Instil doubts, activate reflective thinking Overconfidence, illusion of control Push pop up “You lose” Stop hot cognition; activate reflective thinking Gambler’s fallacy, near-miss fallacy, hot and cold hand streaks Fixed monetary limit Power of defaults; inertia effect on betting decisions Entrapment Self-defined Monetary limits Mental Accounting; House money effect Entrapment If they work we expected less and slower betting and higher self-reported responses for treated subjects compared to the control group
  12. 12. Laboratory experiment findings
  13. 13. Notation
  14. 14. Pre-gamble: behavioural measures
  15. 15. Pre-gamble: self-reported measures (PANAS)
  16. 16. Pre-gamble: self-reported measures (SAM)
  17. 17. In-gamble: behavioural measures
  18. 18. In-gamble: self-reported measures (PANAS)
  19. 19. Online experiment findings
  20. 20. Pre-gamble: behavioural measures
  21. 21. Conclusions ① Pre-gamble treatments can be deemed to be systematically not effective – At least three of the pre-gamble treatments were expected to be more effective than what our findings shows (the two warnings and the overconfidence bias) ② For the other pre-gamble treatments ineffectiveness could be expected from a behavioural perspective (logos, terms & conditions, registration forms) ③ The registration form may have unintended effects and amount to over- regulation ④ In-gamble measures altering the flow between gamblers and machine are fairly effective in systematic ways
  22. 22. g.a.veltri@le.ac.uk @gaveltri

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