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Background and aims: The same information may be perceived differently depending on how it is described. The risk information given on many gambling warning labels tends to accentuate what a gambler might expect to win e.g., "This game has an average percentage payout of 90%,"(return-to-player), rather than what a gambler might expect to lose, e.g., "This game keeps 10% of all money bet on average" (house-edge).. We compared gamblers' perceived chances of winning and levels of warning label understanding under factually equivalent return-to-player and house-edge formats. Design: Online surveys. Experiment 1 was designed to test how gamblers' perceived chances of winning would vary under equivalent warning labels, and Experiment 2 explored how often equivalent warning labels were correctly understood by gamblers. Setting: UK PARTICIPANTS: UK nationals, aged 18 and over and with experience of virtual online gambling games such as online roulette, were recruited from an online crowdsourcing panel (Experiment 1 N = 399, Experiment 2 N = 407). Measurements: The main dependent variables were a gambler's perceived chances of winning on a 7-point Likert scale (Experiment 1), and a multiple-choice measure of warning label understanding (Experiment 2). Findings: The house-edge label led to lower perceived chances of winning in Experiment 1, F(1, 388) = 19.03, p < .001. In Experiment 2, the house-edge warning label was understood by more gamblers (66.5, 95% CI = 60.0%, 73.0%) than the return-to-player warning label (45.6%, 95% CI = 38.8%, 52.4%, z = 4.22, p < .001). Conclusions: House-edge warning labels on electronic gambling machines and online casino games, which explain what a gambler might expect to lose, could help gamblers to pay greater attention to product risk and would be better understood by gamblers than equivalent return-to-player labels.
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Equivalent gambling warning labels are perceived
differently
Philip W. S. Newall
1,2
, Lukasz Walasek
3
&ElliotA.Ludvig
3
Experimental Gambling Research Laboratory, School of Health, Medical and Applied Sciences, Central Queensland University, Melbourne, VIC, Australia,
1
Applied
Psychology, Warwick Manufacturing Group, University of Warwick, Coventry, UK
2
and Department of Psychology, University of Warwick, Coventry, UK
3
ABSTRACT
Background and Aims The same information may be perceived differently, depending on how it is described. The risk
information given on many gambling warning labels tends to accentuate what a gambler might expect to win, e.g. This
game has an average percentage payout of 90%(return-to-player), rather than what a gambler might expect to lose, e.g.
This game keeps 10% of all money bet on average(house-edge). We compared gamblersperceived chances of winning
and levels of warning label understanding under factually equivalent return-to-player and house-edge formats.
Design Online surveys: experiment 1 was designed to test how gamblersperceived chances of winning would vary un-
der equivalent warning labels, and experiment 2 explored how often equivalent warning labels were correctly understood
by gamblers. Setting United Kingdom. Participants UK nationals, aged 18 years and over and with experience of vir-
tual on-line gambling games, such as on-line roulette, were recruited from an on-line crowd-sourcing panel (experiment
1, n= 399; experiment 2, n=407).Measurements The main dependent variables were a gamblers perceived chances
of winning on a seven-point Likert scale (experiment 1) and a multiple-choice measure of warning label understanding
(experiment 2). Findings The house-edge label led to lower perceived chances of winning in experiment 1, F
(1,
388)
= 19.03, P<0.001. In experiment 2, the house-edge warning label was understood by more gamblers [66.5, 95%
condence interval (CI) = 60.0%, 73.0%] than the return-to-player warning label (45.6%, 95% CI = 38.8%, 52.4%,
z=4.22,P<0.001). Conclusions House-edge warning labels on electronic gambling machines and on-line casino
games, which explain what a gambler might expect to lose, could help gamblers to pay greater attention to product risk
and would be better understood by gamblers than equivalent return-to-player labels.
Keywords Behavioural science, electronic gambling machines, framing effect, house-edge, return-to-player, risk
communication.
Correspondence to: Philip Newall,Experimental Gambling Research Laboratory, School of Health, Medicaland Applied Sciences, Central Queensland University,
120 Spencer Street, Melbourne, VIC 3000, Australia. E-mail: p.newall@cqu.edu.au
Submitted 25 June 2019; initial review completed 30 September 2019; nal version accepted 30 December 2019
INTRODUCTION
Firms use their marketing to present their products in the
best light possible. For example, food packaging will often
state that an item is, for instance, 90%-fat-free,which
sounds more attractive than the equivalent description of
10%-fat. Although these descriptions are factually equiva-
lent, food products are evaluated more positively with the
90%-fat-free description than with the 10%-fat description
[1,2]. This is an example of a framingeffect, where judge-
ments are inuenced by how information is described [3].
Here we explore a potential framing effect relevant to gam-
bling warning labels. The United Kingdomsgamblingreg-
ulator, the Gambling Commission, states that remote
virtual gambling games, such as on-line roulette, must
provide information that may reasonably be expected to
enable the customer to make an informed decision about
his or her chances of winning([4] p. 12). Among the op-
tions allowed by the Gambling Commission are two equiv-
alent frames for the gamblers chances of winning: the
return-to-playerand house-edgepercentages.
Despite this regulatory exibility, only the return-to-
player format seems to be in current widespread use, e.g.
This game has an average percentage payout of 90%.A
return-to-player of 90% means that for every £100 wa-
gered, the gambler will receive an average of £90 back.
The same information communicated as a house-edge
would instead state that the game keeps an average of
£10 per £100 wagered. Therefore, return-to-player and
house-edge are factually equivalent frames [5]. Both are
© 2020 The Authors. Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction Addiction
SHORT REPORT doi:10.1111/add.14954
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allowed by the Gambling Commission for virtual on-line
gambling [4] and yet, in practice, only return-to-player
framing appears to be in use.
There is some evidence that gamblers struggle to un-
derstand return-to-player information. A survey of 25 UK
electronic gambling machine (EGM) gamblers found that
only 24% correctly answered a four-alternative multiple-
choice question on return-to-player information correctly
[6]. This failure is worrying, given that the return-to-player
is also displayed on UK EGMs [7]. A qualitative survey of
Canadian EGM gamblers similarly found widespread mis-
understanding around the return-to-player, [8] as has
other qualitative work from the UK [9]. Return-to-player
information is also displayed by law on EGMs in the
Australian state of Victoria [10]. An experimental study
of Australian undergraduates also found a widespread mis-
understanding of the return-to-player [10].
This paper investigates the issue of equivalently framed
gambling warning labels experimentally. Participants were
either given a return-to-player wording or a novel house-
edge reframing of the same information, e.g. This game
keeps 10% of all money bet on average.Foreachexperi-
ment, a pre-registered hypothesis and analysis plan, study
materials, results and analysis output les are available
from https://osf.io/7avnz/. Experiment 1 was run on
31 May 2019, where it was hypothesized that house-edge
framing would lead to a lower perceived chance of winning
than return-to-player framing across a range of typical av-
erage payouts. Experiment 2 was run on 2 June 2019,
where it was hypothesized that gamblers would answer a
four-alternative multiple-choice question correctly more
often with a house-edge than return-to-player label.
EXPERIMENT 1
Participants
A total of 399 UK nationals aged 18 years or older were re-
cruited via Prolic Academic and paid £0.50 each. Partic-
ipants took an average of 3.3 minutes to complete the
study, so this translated to £9.09/hour. Participants were
50.5% female (0.75% preferred not to answer), had a
mean age of 33.9 years (SD = 10.9), a mean problem gam-
bling severity index of 3.7 (SD = 4.7) and gambled an aver-
age of 58.0 days during the last year (SD = 80.4). No other
demographic information was collected.
Participants had earlier indicated to ProlicAcademic
that they had experience in playing at least one on-line vir-
tual casino gambling game.
Design and materials
Using G*Power version 3.1, [11] with the design below, we
estimated that to achieve 95% power, with alpha = 0.01,
three measurements (corr = 0.5) and a small effect size
(f= 0.10), at least 347 participants were required.
On each trial participants were presented with some
short introductory text about on-line gambling and then
a warning label. Figure 1a shows an example from the
return-to-player condition.
Throughout three trials, the magnitude of the house-
edge (return-to-player) was varied to check whether any
potential framing effect was moderated by average payout
size. These were: 5 (95%), 10 (90%) and 15% (85%), re-
spectively. These percentage values were based on the
existing norms for gambling products. Prior EGM research
suggests a house-edge range of 59% (US [12]), 415%
(Canada [13]) and 715% (Australia [14]).
Procedure
Participants were randomly allocated to either the return-
to-player or house-edge condition and completed the three
trials in random order. Participants then completed an at-
tention check with the same warning label, but where
the percentage corresponded to 95% in the house-edge
condition and 5% in the return-to-player condition (which
are implausibly unfair games). Our pre-registered analysis
plan states that any participant giving a higher perceived
winning chance on this attention check than on any previ-
ous trial would be excluded from the analysis, as they may
not have been paying attention.
After the main experimental trials, age, gender, return-
to-player warning label understanding, problem gambling
severity index (PGSI) [15] and last-year gambling fre-
quency (On how many days over the last 12 months have
you gambled?) were collected in random order. The mea-
sure of return-to-player warning label understanding
(which was given to participants in both conditions) is
shown in Fig. 1b (correct answer: For every £100 bet on
this game about £90 is paid out in prizes).
Measures
The dependent variable was the gamblersperceived
chances of winning, as measured by a seven-point Likert
scale (see Fig. 1a).
Results
In total, nine participants failed the attention-check ques-
tion, and were excluded from the analysis (four in the
house-edge condition and ve in the return-to-player
condition).
Data were analysed using a mixed-effects model, to ac-
count for the shared variance between a participantsre-
sponses across different trials. Responses were regressed
on the independent variables of framing (two levels,
between-participants) and magnitude (three levels,
2Philip W. S. Newall et al.
© 2020 The Authors. Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction Addiction
within-participants) and their interaction. In addition, a
random intercept for participants was included in the
model. The tting was performed using the afex package
[16] in R.
Figure 2 presents the mean perceived chance of win-
ning across all levels of the factors. Error bars in the gure
depict 95% condence intervals (CIs) based on the model
t. There was a signicant effect of condition, F
(1,
388)
=19.03,P<0.001, showing that perceived chances
of winning were higher under the return-to-player frame.
There was also a signicant effect of magnitude, F
(2,
776)
=244.85,P<0.001, showing that perceived chances
Figure 1 Example of the (a) main stimulus screen and (b) measure ofreturn-to-player understanding. The main stimulus screen (a) looked identical
to participants in both conditions, except in the house-edge condition the main label was altered to, e.g. This game keeps 10% of all money bet on
average. Participants in both conditions answered the measure of warning label understanding as shown in (b)
Equivalent gambling warning labels 3
© 2020 The Authors. Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction Addiction
of winning were higher for higher values of the return-to-
player. The interaction between the two variables was sig-
nicant, F
(2, 776)
=4.74,P= 0.009. Despite this interac-
tion, inspection of the means in Fig. 2 shows that
responses differed signicantly between the two conditions
across all magnitude levels.
An additional model was run to observe if these effects
remained if gamblerscharacteristics were taken into ac-
count. The model included xed effects of PGSI and
gambling frequency. We tested for the presence of signi-
cant two-way interactions between magnitude, condition,
PGSI and gambling frequency. An analysis of variance
(ANOVA) table is displayed in Table 1. As can be seen, the
only new statistically signicant interaction term was be-
tween PGSI and condition (F=5.34,P= 0.021). Closer in-
spection of the marginal effects revealed a trend such that
those with higher PGSI scores gave higher responses in the
house-edge condition (marginal trend = 0.21, 95%
Figure 2 Mean perceived chance of winning in experiment 1. Perceived chances of winning: 7 = very high chance of coming out ahead, 4 = neither
high nor low chance of coming out ahead, 1 = very low chance of coming out ahead. Error bars represent 95% condence intervals
Table 1 Mixed-model analysis of variance (ANOVA) table.
Model 1 Model 2
Variable F-value P-value F-value P-value
Condition 19.03 (<0.001) 21.15 0
Magnitude 244.85 (<0.001) 245.72 (<0.001)
Magnitude × condition 4.74 (0.009) 4.92 (0.008)
Problem gambling severity <0.01 (0.994)
Gambling frequency 5.94 (0.015)
Problem gambling severity × condition 5.34 (0.021)
Gambling frequency × condition 0.04 (0.843)
Problem gambling severity × magnitude 2.25 (0.106)
Gambling frequency × magnitude 1.18 (0.307)
Problem gambling severity × Gambling frequency 0.42 (0.517)
F-values and P-valuesin parentheses, for a model that compares experimentally manipulated variables (model 1) and a model that adds individual difference
variables and two-way interactions (model 2), showing main effects and interactions, with interactions denoted by *.
4Philip W. S. Newall et al.
© 2020 The Authors. Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction Addiction
CIs = 0.06; 0.48), but lower responses in the return-to-
player condition (marginal trend = 0.21, 95%
CIs = 0.46; 0.04.
Overall, 47.4% of participants responded correctly to
the multiple-choice question of return-to-player under-
standing. As can be seen in Table 2, the most-commonly
given incorrect answers were: 90% of people who play this
game will win something(23.8%) and: This game will
give out a prize 9 times in 10(23.8%).
Discussion
Participants rated their perceived chances of winning as
higher in the return-to-player condition than the house-
edge condition. Perceived chances of winning are subjec-
tive, however, and hence there is no correctresponse to
experiment 1. Experiment 2 was designed to address this
limitation, by assessing whether participants would answer
the four-alternative multiple-choice question correctly
more often with a house-edge than return-to-player label.
EXPERIMENT 2
In total, 407 participants were recruited (56.8% female,
mean age = 33.7 years, mean PGSI = 3, SD = 4.6, mean
days gambled over previous 12 months = 55.6, SD = 78.3).
No other demographic information was collected. Partici-
pants were paid £0.25 and took an average of 2.0 minutes
to complete the study, which translates to £7.50/hour. Par-
ticipants were given either a return-to-player or house-
edge warning label (both equivalent to a house-edge of
10%), and asked to complete the multiple-choice measure
of understanding used in the previous experiment. In the
previous experiment this measure of understanding was
given to all participants with the return-to-player warning
label only, but here understandingof both labels (return-to-
player and house-edge) was assessed.
Results
In total, 66.5% (95% CI = 60.0%, 73.0%) of participants in
the house-edge condition answered the understanding
question correctly, which was shown by logistic regression
to be signicantly more than the 45.6% (95% CI = 38.8%,
52.4%) of participants in the return-to-player condition
(z=4.22,P<0.001). Table 2 provides a breakdown of re-
sponses to this measure across the two experiments. The
house-edge condition was associated with a large shift
away from the incorrect response: This game will give
out a prize 9 times in 10.
A model was run to observe if this effect remained if
gamblerscharacteristics were taken into account. A logis-
tic regression model was run controlling for PGSI and gam-
bling frequency and including interaction terms between
experimental condition and these two individual difference
variables. There was an additional signicant main effect of
PGSI, whereby gamblers with higher PGSI levels were
more likely to answer the question correctly in either label
condition [z=2.18,P=0.030,oddsratio(OR)=1.08].
However, neither the interaction term on PGSI severity
(z=1.31, P= 0.190) nor gambling frequency
(z=0.60, P= 0.545) was statistically signicant. There-
fore, the house-edge warning label was understood more
clearly by all gamblers.
GENERAL DISCUSSION
The present ndings contribute to the literature on gam-
bling warning labels [17]. Gamblersperceived chances of
winning were signicantly lower under the house-edge
warning label than a return-to-player warning label in ex-
periment 1. Perceived chances of winning are subjective,
and hence there is no correctresponse to experiment 1.
Experiment 2 addressed this limitation, and showed that
more gamblers correctly understood the house-edge label
than the return-to-player label. Given the international ev-
idence base showing that return-to-player information is
frequently misunderstood, [6,8,10] these results suggest
that it would be better to display house-edge information
instead in jurisdictions such as the United Kingdom [4] or
Victoria, Australia [10].
Measures of gambling behaviour in a realistic gambling
task would help to provide further support to the practical
policy relevance of these results. Warning labels on UK
EGMs and virtual on-line gambling games, however, are
currently only found on low-prominence help screens,
Table 2 Responses to the measure of warning label understanding.
Response Experiment 1
Experiment 2
(return-to-player condition)
Experiment 2
(house-edge condition)
90% of people who play this game will win something23.8% 18.1% 16.3%
This game will give out a prize 9 times in 1023.8% 32.8% 10.3%
If you bet £1 on this game you are guaranteed to win 90p5.0% 3.4% 6.9%
Correct response: For every £100 bet on this game about £90 is paid
out in prizes
47.4% 45.6% 66.5%
Equivalent gambling warning labels 5
© 2020 The Authors. Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction Addiction
which many regular gamblers have not even seen [6]. To-
bacco control research suggests that effective warning la-
bels must be more prominent [18]. Additional changes
may be required to yield measurable changes in gamblers
behaviour in real gambling environments. More research
is also required for gamblers at the highest levels of
problem gambling severity, and to explore other gambler
subtypes who might respond differently to the framing
manipulation.
While these results suggest that house-edge informa-
tion is a better way to communicate gambling risks, even
better information formats are surely possible. For example,
graphical risk representations can be more effective than
equivalent numerical information [19]. House-edge infor-
mation might be even better understood with visual aids.
These results provide evidence for a novel framing effect
in gambling warning labels. This further supports the view
that gambling policy should reect behavioural scientic
insights [2022].
Declaration of interests
In 2018, P.W.S.N. was included as a named researcher on a
grant funded by GambleAware, and in 2019 received
travel and accommodation funding from the Spanish
Federation of Rehabilitated Gamblers. E.A.L. was co-
investigator on a grant funded by the Alberta Gambling Re-
search Institute that ended in February 2019.
Acknowledgements
We thank the Behavioural Science Global Research Priori-
ties fund from the University of Warwick for funding and
Depi Alempaki and Derek Webb for their helpful ideas.
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... On the basis of behavioral science research into the importance of framing to the interpretation of messages, [20][21][22] we thought that the gambling warning information could be provided in a more understandable way, 23 namely, by speaking in terms of how much money the house keeps-that is, by using house-edge framing. We therefore conducted an experiment comparing the two messaging approaches. ...
... In a study with almost 400 participants, 66.5% of gamblers who read a house-edge statement (which explicitly said that the operator keeps an average of 10% of the money it takes in) selected the correct interpretation of this information in a multiple-choice question, compared with 45.6% of gamblers who were given the equivalent return-to-player statement indicating that an average of 90% of bet money is returned to players. 23 The largest difference was that 32.8% of gamblers given the return-to-player statement incorrectly selected the option of "This game will give out a prize 9 times in 10," whereas just 10.3% of gamblers given the house-edge statement chose that interpretation. In another study involving 407 gamblers, participants perceived a lower chance of winning when they were provided with house-edge information than when they received equivalent return-toplayer information. ...
... In another study involving 407 gamblers, participants perceived a lower chance of winning when they were provided with house-edge information than when they received equivalent return-toplayer information. 23 These divergent interpretations have a significant influence on betting behavior, according to a recent experiment involving more than 2,400 experienced American gamblers. 24 Participants were given small amounts of money to keep or gamble with (in the hope of making more); those who chose to gamble played an online slot machine. ...
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Legal gambling is a large industry in many countries. One way some governments try to protect people from losing more than they can afford is by requiring warning labels on gambling machines and their online equivalents. Prominent labels that make the odds of winning clear serve as nudges: They promote a beneficial behavior (such as deciding that the risk of losing money is too high) without interfering with choice (such as by restricting the availability of gambling). However, if gambling operators use labels that are difficult to understand, find, or read, those messages instead hamper decision-making and thus become sludge. In this article, we report on new research into whether gambling labels in the world’s largest regulated online gambling market (the United Kingdom) are more consistent with nudge or sludge. We found that gambling operators overwhelmingly used sludge strategies when posting required gambling warning labels: For instance, they framed the message using a confusing format, applied a small font size to the text, and placed the warning on obscure help screens. We therefore propose that public policy officials throughout the world establish requirements for the wording and presentation of gambling warning labels to ensure that gamblers are well-informed about the odds they face.
... Equivalently reframed "house-edge" information, e.g. "This game keeps 10% of all money bet on average" resulted in lower perceived chances of winning in both community (Newall, Walasek, & Ludvig, 2020a) and treatment-seeking samples (Newall, Walasek, Ludvig, & Rockloff, 2020), and was more accurately understood by community gamblers than return-to-player information (Newall et al., 2020a). ...
... Equivalently reframed "house-edge" information, e.g. "This game keeps 10% of all money bet on average" resulted in lower perceived chances of winning in both community (Newall, Walasek, & Ludvig, 2020a) and treatment-seeking samples (Newall, Walasek, Ludvig, & Rockloff, 2020), and was more accurately understood by community gamblers than return-to-player information (Newall et al., 2020a). ...
... However, the literature comparing return-to-player versus house-edge information (Newall et al., 2020a) and the effect of volatility warnings (Newall et al., 2020b), is so-far based on gamblers' self-reports on how this information might affect their gambling. This is an important limitation, as gamblers might intend to make changes that they cannot enact (Sheeran, 2002). ...
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Cost-of-play information is one public health intervention recommended to help reduce gambling-related harm. In the UK, this information is given on electronic gambling machines in a format known as the “return-to-player”, e.g., “This game has an average percentage payout of 90%.” However, previous evidence suggests that this information could be improved by equivalently restating it in terms of the “house-edge”, e.g., “This game keeps 10% of all money bet on average.” A “volatility warning,” stating that this information applies only in the statistical long-run, has also been recommended to help gamblers understand cost-of-play information. However, there is no evidence comparing these information provisions’ effect on gamblers’ behavior. An experiment tested US gamblers’(N=2,433) incentivized behavior in an online slot machine, where this information was manipulated between-participants along with a counter showing the total amount bet. Preregistered analyses showed that participants gambled significantly less when house-edge information or a volatility warning were shown compared to standard return-to-player information, with no effect of the total amount bet counter, and no significant interaction effects. However, these significant findings had small effect sizes, suggesting that a public health approach to gambling should not rely on informational provisions only. Subject to supportive evidence from more ecologically-valid designs such as field studies, these results suggest that improved cost-of-play information could lead to reduced rates of gambling expenditure and therefore benefit a public health approach to gambling.
... "This game keeps 10% of all money bet" resulted in lower perceived chances of winning in both community (Newall, Walasek, & Ludvig, 2020a) and treatment-seeking samples (Newall, Walasek, Ludvig, & Rockloff, 2020), and was more accurately understood by community gamblers than return-to-player information (Newall et al., 2020a). ...
... "This game keeps 10% of all money bet" resulted in lower perceived chances of winning in both community (Newall, Walasek, & Ludvig, 2020a) and treatment-seeking samples (Newall, Walasek, Ludvig, & Rockloff, 2020), and was more accurately understood by community gamblers than return-to-player information (Newall et al., 2020a). ...
... However, the literature comparing return-to-player versus house-edge information (Newall et al., 2020a) and the effect of volatility warnings (Newall et al., 2020b), is so-far based on gamblers' self-reports on how it might affect their gambling. This is an important limitation, as gamblers might intend to make changes that they cannot enact (Sheeran, 2002). ...
Preprint
Cost-of-play information is one public health intervention recommended to help reduce gambling-related harm. In the UK, this information is given on electronic gambling machines in a format known as the “return-to-player”, e.g., “This game has an average percentage payout of 90%.” However, previous evidence suggests that this information could be improved by equivalently restating it in terms of the “house-edge”, e.g., “This game keeps 10% of all money bet.” A “volatility warning,” stating that this information applies only in the statistical long-run, has also been recommended to help gamblers understand cost-of-play information. However, there is no evidence comparing these information provisions’ effect on gamblers’ behavior. An experiment tested US gamblers’(N=2,433) incentivized behavior in an online slot machine, where this information was manipulated between-participants along with a counter showing the total amount bet. Preregistered analyses showed that participants gambled significantly less when house-edge information or a volatility warning were shown compared to standard return-to-player information, with no effect of the total amount bet counter, and no significant interaction effects. These results suggest that improved cost-of-play information could benefit a public health approach to gambling.
... However, exposure to any of the experimental conditions did not result in a greater understanding of return to player than controls. Newall, Walasek, and Ludvig (2020) conducted an RCT's in the UK on the impact of education focused on return to player or house-edge on perceived chances of winning. This study reported house-edge was better understood by gamblers than return to player and had a stronger association with perceived chances of winning. ...
... To exploit the full potential of self-exclusion as a measure of gambler protection, its acceptance and its utilization need to be increased by target-group-specific information addressing financial issues and the role of significant others, simplifying the administrative processes, facilitating selfexclusion at an early stage of the gambling career, offering self-determined exclusion durations, and promoting additional use of professional addiction care. Newall et al., 2020 UK Test how gamblers perceived chances of winning would vary with different warning labels. ...
Technical Report
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This review builds on the 2018 NSW gap analysis by reviewing recent Australian and, where relevant, international gambling research to identify areas in which evidence and/or knowledge is minimal or lacking. The findings of the current review will inform the Responsible Gambling Fund’s prioritisation of research projects for their 2020-2021 research plan. The methodology included a rapid literature review of 198 peer-reviewed articles and 17 grey literature reports published between 1 October 2018 and 23 April 2020. Key findings (i) The research on emerging technologies is limited, although there are consistent findings that gaming and gambling are increasingly converging, and of an association between in-game purchases and gambling problems (ii) There is sufficient evidence of the associations between advertising and gambling behaviours to support gambling advertising being a priority for policy and regulation (iii) The evidence base on the effects of prevention and harm reduction intervention is dominated by evaluations of individual-level initiatives, with a paucity of research on supply reduction interventions (iv) There was disagreement in the literature on the definition, conceptualisation and measurement of harm. Further gambling harm measurement tools are needed to support the design and measurement of prevention, harm minimisation and treatment research. (v) Vulnerable groups were consistently found to have lower gambling participation but higher rates of gambling problems.Vulnerable groups were identified as young people, people from culturally and linguistically diverse communities, Aboriginal people, at-risk professions, and groups susceptible to family violence and other co-morbidities.
... UK alcohol companies have not always met their voluntary pledges around health warnings (Petticrew et al., 2016;Alcohol Health Alliance UK, 2020). Gambling companies' disclosures about the odds of winning are provided in an arguably suboptimal risk communication format (Newall et al., 2020). Sludge in the loot box probability disclosure context could be implemented in various ways through the use of 'obscurant friction', which obscures the consumers' understanding, rather than promotes it (Mills, 2020). ...
... However, the actions of some video game companies do at least seem to draw parallels with the arguably socially irresponsible corporate actions in other, more established addictive areas. For example, the alcohol (Petticrew et al., 2016), gambling (Newall et al., 2020), and tobacco industries (Hiilamo et al., 2012) have all taken various actions that likely reduce the effectiveness of their product warnings. The actions of most video game companies in this study appear to be more consistent with ideas of 'sludge' (Thaler, 2018;Sunstein, 2020) or 'dark nudges' (Newall, 2019;Petticrew et al., 2020), which inhibit optimal consumer choice, than with the traditional conceptualization of 'nudge' (Thaler & Sunstein, 2008), which aims to improve consumer choice. ...
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Loot boxes provide randomized rewards in video games; their purchase is linked to disordered gambling and they are present in approximately half of UK video games. The relative novelty of loot boxes means that regulators and policymakers in various jurisdictions are still deciding how to regulate them. The People's Republic of China (PRC) is the first, and presently only, jurisdiction to legally require companies to disclose the probabilities of obtaining randomized loot box rewards-an approach that is also favored by the industry as self-regulation. This study is the first to assess paid loot box prevalence in the PRC and companies' discretionary interpretations of probability disclosure regulations. Loot boxes were found in 91 of the 100 highest-grossing PRC iPhone games. Of games deemed suitable for children aged 12+, 90.5% contained loot boxes. Probability disclosures could not be found for 4.4% of games containing loot boxes. Disclosures were implemented through various methods both in-game and on the games' official websites; however, consistent with the concept of 'sludge,' only 5.5% used the most prominent format of automatically displaying the probabilities on the in-game loot box purchase page. Loot box probability disclosures should be uniform and visually prominent to best help inform consumers.
... Reverse withdrawals and high suggested deposit limits are just two examples of online gambling sludge. Another recent example, made in this literature, is how mandated cost-of-play information is placed in a frequently misunderstood format [18] at the bottom of difficult to navigate help screens [19]. Although it would be beneficial to nudge gamblers toward safer choices, the prevention of both current and potential sludge practices should be of higher urgency in the agendas of those who want to promote safer gambling. ...
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Problem gamblers discount delayed rewards more rapidly than do non-gambling controls. Understanding this impulsivity is important for developing treatment options. In this article, we seek to make two contributions: First, we ask which of the currently debated economic models of intertemporal choice (exponential versus hyperbolic versus quasi-hyperbolic) provides the best description of gamblers’ discounting behavior. Second, we ask how problem gamblers differ from habitual gamblers and non-gambling controls within the most favored parametrization. Our analysis reveals that the quasi-hyperbolic discounting model is strongly favored over the other two parametrizations. Within the quasi-hyperbolic discounting model, problem gamblers have both a significantly stronger present bias and a smaller long-run discount factor, which suggests that gamblers’ impulsivity has two distinct sources.
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The premise of this article is that an understanding of behavioural economics can inform and improve the effectiveness of gambling policies and practices. Existing interventions to minimise gambling-related harms appear to be ineffective. Many conceptual models of gambling consider the role of cognitive distortions in potentiating harmful gambling outcomes, however, policies and practices often fail to recognise the heuristics (and resulting biases) that drive ongoing gambling. A behavioural economics approach to gambling public policies and interventions acknowledges humans do not always act in their best interest and introduces a range of policy tools that better motivate behaviour change. This paper reviews insights from psychology and behavioural economics to develop recommendations for gambling harm-minimisation policies. Behavioural science tools such as commitment devices, personalised messaging, and more generalised ‘nudges’ can be effective across the entire spectrum of gambling-related harms. The interventions recommended involve low-cost, subtle tweaks to the decision-making environment that promote agency, encourage positive behavioural change, and improve measurable outcomes. A multidisciplinary, evidence-based approach to developing gambling policies is recommended to enhance gamblers’ well-being.
Technical Report
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The aim of this report is to review evidence and theory regarding the gambling product through its structural characteristics (i.e., the ‘agent’ component of the epidemiological triangle). By providing a better understanding of structural characteristics, stakeholders should be better equipped to promote and evaluate responsible gambling and harm-minimisation strategies. Structural characteristics are essentially the building blocks of a gambling game. They are the basis for their differential appeal depending on how they satisfy different needs for different consumers. They combine with environmental and individual factors to determine both positive and negative outcomes of gambling participation. Structural characteristics vary considerably from game to game and evolve quickly in response to changes in technology; this renders associated policymaking challenging. The report is structured to consider categories of structural characteristics. Within each section we consider the theory and evidence concerning the possible links between characteristics and gambling problems, together with potential implications for specific interventions that may merit consideration by regulators and commercial gambling providers.
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