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BRIEF REPORT
Cognitive Dissonance, Personalized Feedback, and Online
Gambling Behavior: An Exploratory Study Using
Objective Tracking Data and Subjective Self-Report
Michael Auer
1
&Mark D. Griffiths
2
Published online: 20 September 2017
#The Author(s) 2017. This article is an open access publication
Abstract Providing personalized feedback about the amount of money that gamblers have
actually spent may—in some cases—result in cognitive dissonance due to the mismatch
between what gamblers actually spent and what they thought they had spent. In the present
study, the participant sample (N= 11,829) was drawn from a Norwegian population that had
played at least one game for money in the past six months on the Norsk Tipping online
gambling website. Players were told that they could retrieve personalized information
about the amount of money they had lost over the previous 6-month period. Out of the
11,829 players, 4045 players accessed information about their personal gambling expen-
diture and were asked whether they thought the amount they lost was (i) more than
expected, (ii) about as much as expected, or (iii) less than expected. It was hypothesized
that players who claimed that the amount of money lost gambling was more than they had
expected were more likely to experience a state of cognitive dissonance and would attempt
to reduce their gambling expenditure more than other players who claimed that the amount
of money lost was as much as they expected. The overall results contradicted the hypothesis
because players without any cognitive dissonance decreased their gambling expenditure
more than players experiencing cognitive dissonance. However, a more detailed analysis of
the data supported the hypothesis because specific playing patterns of six different types of
gambler using a machine-learning tree algorithm explained the paradoxical overall result.
Keywords Behavioral tracking .Gambling .Cognitive dissonance .Gambling expenditure .
Online gambling
Int J Ment Health Addiction (2018) 16:631–641
DOI 10.1007/s11469-017-9808-1
*Mark D. Griffiths
mark.griffiths@ntu.ac.uk
Michael Auer
m.auer@neccton.com
1
neccton Ltd., Office 404 Albany House, 324 Regent Street, London W1B 3HH, UK
2
International Gaming Research Unit, Psychology Department, Nottingham Trent University, 50
Shakespeare Street, Nottingham NG1 4FQ, UK
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Gambling is a popular activity found in many cultures (Calado and Griffiths 2016). Surveys
have reported that most individuals gamble but do so infrequently (e.g., Bernstein 1996;
Wardle et al. 2012), and that most people engage in gambling at some point during in their
lives (Meyer et al. 2009;Orfordetal.2003). In Great Britain, nationally representative surveys
have found that over two-thirds of the population have engaged in at least one type of
gambling in the previous 12 months (Wardle et al. 2012) including both offline and online
gambling. A small proportion of gamblers become overly involved in terms of the amount of
money and time they spend (e.g., Kessler et al. 2008; Petry et al. 2005). However, internet
gambling is accessible 24/7, and potentially negative psychosocial impacts occur among a
small minority of individuals. This is because in addition to individuals’vulnerability factors,
various structural and situational characteristics (e.g., accessibility, affordability, and anonym-
ity) may increase the risk of individuals developing a gambling problem (Griffiths 2003;
McCormack and Griffiths 2013). Consequently, gamblers need to be educated about how to
play more responsibly, and vulnerable groups need to be protected. One way in which to get
players to gamble more responsibly is the use of personalized feedback about their actual
gambling behavior.
Responsible gambling tools (e.g., limit-setting tools, pop-up messages, personalized feed-
back, temporary self-exclusions) are a way of facilitating players to gamble in a more
responsible manner (Auer and Griffiths 2013; Griffiths et al. 2009; Harris and Griffiths
2017). In two studies using behavioral tracking data, Auer and Griffiths (2014,2015a) showed
that a feedback pop-up message which appeared after 1000 consecutive online slot games had
a small but significant effect on the number of players who terminated their current playing
session (i.e., significantly more gamblers ceased their playing session after seeing a pop-up
message informing them of how many consecutive games on a slot machine they had played
compared to controls).
Personalized feedback that informs gamblers about their past playing behavior incor-
porating a longer time period than just the current session has been empirically investi-
gated in three real-world studies using behavioral tracking data (Auer and Griffiths 2015b,
2016a;Wohletal.2017). Auer and Griffiths (2015b) studied the behavior of online
gamblers in relation to their voluntary use of a responsible gaming behavioral tracking
tool compared with a matched control group of gamblers (that had not used the behavioral
tracking tool) 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
[Auer et al. 2012; Auer and Griffiths 2014]). The results demonstrated that online
gamblers receiving personalized feedback spent significantly less money and time gam-
bling in comparison to those that did not receive personalized feedback (i.e., the matched
controls).
An experimental study conducted by Auer and Griffiths (2016a) with online gamblers
from the Norwegian operator Norsk Tipping manipulated the effect of three different types
of personalized feedback (personalized feedback, normative feedback, and/or a recommen-
dation). The players were randomly assigned to the specific types of feedback. Compared to
the control group (receiving no feedback at all), all groups that received some kind of
feedback significantly reduced their gambling behavior as assessed by theoretical loss,
amount of money wagered, and gross gaming revenue. The results supported the hypoth-
esis that personalized behavioral feedback enables behavioral change in gambling but that
normative feedback did not appear to change behavior significantly more than personalized
feedback.
632 Int J Ment Health Addiction (2018) 16:631–641
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Wohl et al. (2017) investigated the use of a responsible gambling tool that provided
personalized feedback to players about how much they had won or lost during a three-
month period. Using tracking data from electronic gaming machine (EGM) players provided
by the Canadian gambling operator Ontario Lottery and Gaming, Wohl et al. found that when
players were asked to state whether they thought that the actual amount lost was more or less
than they had expected, players who underestimated their losses (i.e., those who lost more
money than they thought) reduced the amount they wagered and the amount they lost in the
subsequent three months. These results suggest that informing gamblers about their expendi-
ture appears to change subsequent behavior.
Providing information about the amount of money that gamblers have actually spent may—
in some cases—result in cognitive dissonance due to the mismatch between what gamblers
actually spent and what they thought they had spent. According to the theory of cognitive
dissonance (Festinger 1957), individuals tend to seek consistency among their cognitions.
Dissonance occurs when there is an inconsistency between attitudes or behaviors. To reduce
dissonance, individuals then have to do something to eliminate the dissonance. Festinger
(1957) stated that existence of cognitive dissonance (i.e., being psychologically uncomfort-
able) motivates individuals to try reducing their dissonance in order to achieve consonance. As
far as the present authors are aware, no previous study has applied the theory of cognitive
dissonance to the perception of gambling behavior.
The present study explored changes in actual gambling behavior by examining whether
the amount of money lost gambling was (i) more than expected, (ii) about as much as
expected, or (iii) less than expected. For the purposes of the present paper, it was assumed
that the answer to this question determined the degree of cognitive dissonance due to the
difference between subjective and objective loss of money while gambling (i.e., the greater
the difference between what the person thought they had spent versus what they had
actually spent would mean greater cognitive dissonance). The methodological approach
of the present study was exploratory. However, it was hypothesized that players who
claimed that the amount lost was more than they had expected were more likely to
experience a state of cognitive dissonance and would therefore attempt to reduce the
amount of money they spend gambling compared to players who claimed that the amount
of money lost was as much as they expected.
Methods
Participants
The participant sample was drawn from the population that had played at least one game for
money on the Norsk Tipping online platform (Instaspill) during April 2015. A total of 11,829
players were randomly selected from 69,631 players that fulfilled the selection criteria (see
next section). Of these, 8182 were males (69.1%) and 3647 were females (30.9%). The mean
average age was 40.52 years (SD = 13.19 years). Approximately 25% of the customers were
younger than 30 years, and 25% were aged over 50 years. There was no significant age
difference between males and females (t= 1.376, df = 7194, p= 0.169). The 11,829 players
were sent an email which notified them about the availability of information that was
personally relevant to them. It was up to players to click on a link in the email and retrieve
the information if they so wished.
Int J Ment Health Addiction (2018) 16:631–641 633
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Sampling
The participants only comprised players who had a net loss across all games in the past month
before the study commenced (i.e., recent winners were excluded because the goal of the study
was to research cognitive dissonance among players who had recently lost). Those who had
self-excluded and/or taken a break from gambling were also excluded from subsequent
analyses because these players had not gambled. More specifically, the sample was drawn
based on the amount lost across all games (online casino, sports betting, lottery, etc.). The
amount of money lost by each player was computed by simply subtracting the total amount
wagered from the total amount won. The overwhelming majority of players lost only small
amounts of money. Therefore, in order to examine the impact of messaging on high-intensity
players, there was an oversampling of high-intensity gamblers.
Personalized Feedback
A simple personalized message was sent to the 11,829 players that said: BHow much do you
think you spent on gambling recently? Our records show that you lost [XXX] NOK last
month.^In addition, players were also presented with an illustrated line chart that contained
the monthly values for their personal losses over the previous six-month period. Players were
also told that they could retrieve the information any time during the following month. Out of
the 11,829 players, 4045 players clicked on the link in the email and viewed their personalized
information. After players viewed their personal loss, they were also asked a series of
questions. Among them they were asked whether they thought the amount lost was (i) more
than expected, (ii) about as much as expected, or (iii) less than expected. As noted above, it
was assumed that the response to this question determined the degree of cognitive dissonance
(i.e., the greater the difference between subjective and objective loss of money while gambling,
the greater the cognitive dissonance).
Analysis
Assessing whether personalized feedback resulted in the desired behavioral change meant that
behavioral change had to be assessed via specific variables and via specific time periods. In the
present study, it was decided that gambling behavior seven days prior to the intervention would
be compared with gambling behavior seven days after the intervention message was read. This
is because changes over a shorter time period would most likely only be due to chance, and
changes over longer time periods would not be expected based on the type of feedback
provided.
The measure of behavior that was used to assess gambling behavior was theoretical loss
(TL). TL is the amount of money wagered and risked (i.e., amount of money staked multiplied
by the probability of winning). The TL statistic was computed as BTL_after^minus
BTL_before^divided by BTL_before.^This statistic reflects the change in behavior seven days
after the message was read as a percentage of the behavior 7 days before the message was read.
This procedure helps assess the individual change as independent from the intensity of play as
much as possible. A negative value indicates a decrease in gambling behavior and a positive
value indicates an increase in gambling behavior. A value of 0 means that no change in
gambling behavior occurred at all. A value of (say) −0.5 means that the gambling behavior
decreased by 50% compared to seven days before. The statistic ranges from −1to+infinity.A
634 Int J Ment Health Addiction (2018) 16:631–641
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value of −1 means that the player did not engage in gambling seven days after the message
was read. Avalue of + 10 means that the gambling behavior increased 1000% in the seven days
after the message was read compared to seven days before the message was read.
Results and Preliminary Discussion
In total, 63% of the players reported that the amount lost was about as much as they expected.
Three in 10 players (30%) reported that they lost more than expected, and 7% reported that
they lost less than expected (see Table 1). Players who reported that they had lost more than
expected also spent more money 30 days prior than the other two groups (X
2
= 80.018, df = 1,
p< 0.001). The breakdown of differences between gender and age are shown in Tables 2and 3
respectively.
Given that the aim of the present study was to explore whether cognitive dissonance leads to
change in actual gambling behavior, it was assumed that players who spent more gambling than
they thought should decrease the amount of money they spend gambling compared to others.
The decrease in the amount of money spent gambling would be the most obvious way for
players to reduce cognitive dissonance. If players behave as expected, then those players who
reported that the amount of money they had lost was more than expected should decrease the
amount of money spent gambling more than players who claim that it is about as much as
expected. However, this is not the case. On the contrary, players who reported that the amount
of money they had lost was more than they expected actually decreased their gambling
expenditure less than those who reported it was about as much as expected (see Table 4).
Players who reported that the feedback amount was even less than they expected decreased their
gambling behavior the most (see Table 4). These results contradict the hypothesis that cognitive
dissonance leads to decreased gambling expenditure (Χ
2
= 18.105, df = 2, p< 0.0001).
However, the change statistic which expresses the change in behavior after the message
as a percentage of the behavior before the message was read is not entirely independent of
the intensity of play. For this reason, further analysis assessed this difference among
groups of equal intensity of play. Consequently, players were divided into two groups: a
group of the 90% lowest spenders and a group of the 10% highest spenders. Table 5shows
the median change in theoretical loss separately for the two gambling intensity groups.
Players who thought their actual monetary losses were higher than what they expected
actually decreased their expenditure compared to those who thought their actual monetary
losses were about as high as they expected. This was true among the 90% of lower spenders
(n= 3635) as well as among the 10% highest spenders (n= 410). This was significant for
Tab le 1 Percentages of players (n= 4045) who reported they had gambled more or less money than they
expected or had spent about the same
Median theoretical loss Number
More than expected 774 1236 (31%)
About as much as expected 507 2547 (63%)
Less than expected 377 262 (6%)
Total 556 4045 (100%)
The percentage of females reporting that they had spent more than they expected (see Table 2) was higher than in
males (X
2
= 12.358, df = 2, p< 0.002). The median loss for females 30 days before was NOK 696 and the
corresponding amount for males was NOK 364 (X
2
= 207.15, df = 1, p< 0.001)
Int J Ment Health Addiction (2018) 16:631–641 635
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the players with the 90% lowest gambling expenditure (Χ
2
= 15.586, df = 2, p< 0.001) but not
significant for the players with the 10% highest gambling expenditure (Χ
2
= 2.0139, df = 2,
p< 0.3653).
This means that players who reported that they had lost more money than they expected
decreased their gambling expenditure less than players who reported that their monetary losses
were about what they expected. In order to better understand this paradox, further data analysis
was carried out. More specifically, a recursive tree algorithm was applied. This is a machine-
learning method that groups data according to predefined criteria. In the present study, the
players’opinion concerning the actual amount of money lost gambling was used to train the
algorithm. The main aim was to determine differences between players who reported that they
lost more money than expected and players who reported that they lost about as much as
expected. For this reason, the third group of players who reported having lost less than they
expected was discarded from further analysis. The goal of the algorithm was to search for
groups of players who predominantly chose one of the two possible answers. Six different
groups of gamblers with unique patterns of play were extracted using the algorithm. Table 6
describes the pattern (average values) for each of the six groups. The row labeled BResponse^
contains the dominant answer provided in that particular group. The median seven-day change
across all players who answered the question was −42%. The most significant decrease lay
within Group 6, which is also the by far the largest group with 1745 players out of 3783 (46%).
This group’smedianchangewas−69% and therefore contributed the most to the overall
median change of −42%.
Group 1: These players predominantly answered that they spent more than they thought and
therefore are expected to experience cognitive dissonance. These are young players
(mean = 32 years), with above average losses (NOK 792) who prefer sports betting and playing
casino games. The response variable labeled BTL trend^indicates whether the theoretical loss
over the past six months is steadily increasing. On average, 28% of players show a significant
increase. In this group, 51% of players show a significant increase. Players in Group 1 also self-
Tab le 2 Percentages of players (n= 4045) who reported they had gambled more or less money than they
expected or had spent about as much as expected by gender
More than expected About as much Less than expected Total
Female 403 (34%) 701 (60%) 71 (6%) 1175
Male 833 (29%) 1846 (64%) 191 (7%) 2870
Total 1236 (31%) 2547 (63%) 262 (6%) 4045
The average age across the two first groups was 42 years (see Table 3), and players who reported that they lost
less than expected were on average 39 years which was statistically significant (F= 6.246, df = 4043, p=0.0125)
Tab le 3 Percentages of players (n= 4045) who reported they had gambled more or less money than they
expected or had spent about as much as expected by age
Mean age Number
More than expected 42 1236
About as much as expected 42 2547
Less than expected 39 262
4045
636 Int J Ment Health Addiction (2018) 16:631–641
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excluded (30%) more often in the past than the average player (22%). The response variable
labeled BPlayscan rating^indicates the percentage of players who were categorized Bat risk^of
problem gambling by the behavioral tracking system Playscan used by the gambling operator
Group 2: These players predominantly answered that they spent more than they thought
they had and therefore are expected to experience cognitive dissonance. These are female
players with above average losses (NOK 730) who prefer gambling on scratchcards. They are
also at risk of problematic gambling as identified by the behavioral tracking tool Playscan used
by the gaming operator
Group 3: These players predominantly answered that they spent about as much as they
thought and therefore are not expected to experience cognitive dissonance. These are male
players with the highest losses (NOK 1591) across all six groups who prefer casino games and
have had voluntary self-exclusions from gambling in the past. They are also at risk of problem
gambling as identified by Playscan
Group 4: These players predominantly answered that they spent about as much as they
thought and therefore are not expected to experience cognitive dissonance. These are lottery
players with low losses (NOK 444)
Group 5: These players predominantly answered that they spent about as much as they
thought and therefore are not expected to experience cognitive dissonance. These are male
players who engage in casino games, sports betting, and VLT gambling. They have had
voluntary self-exclusions from gambling in the past. However, they also have had a net win
(NOK 3195) over the last week before the message was sent as can be seen by the positive loss
value. These players are very specific as they managed to generate a net win over the last
week, although their gambling involvement is very high. Their answer can be explained as the
feedback, which informs them about high losses over the last months is not in line with their
recent experiences during which they managed to win
Group 6: These players predominantly answer that they spent about the same as they
thought and therefore are not expected to experience cognitive dissonance. These are lottery
Tab le 4 Change in theoretical loss among players (n= 4045) seven days after they received personalized
feedback about their gambling behavior
Change in theoretical loss Number
More than expected −35% 1236
About the same −44% 2547
Less than expected −56% 262
Tot al −42% 4045
Tab le 5 Change of theoretical loss seven days after accessing a personalized message among high-intensity
gamblers (n= 410) and low-intensity gamblers (n=3635)
Intensity group Answer Change TL TL before Number
Low 90% More −35% 204 1075
About as much −44% 188 2320
Less −56% 167 240
Top 10% More −34% 1891 161
About as much −45% 2254 227
less −65% 1981 22
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and scratchcard players with low involvement (NOK 280) and a low number of playing days
in the last month (n= 5). This is by far the largest group of players in the present study.
General Discussion
The present study investigated the effect of cognitive dissonance on gambling behavior in a
group of real-world players at a real online gambling site. Players were provided with visual and
numerical feedback about their gambling losses and then asked if their actual monetary loss was
more than they expected, about as much as expected, or less than expected. The present authors
hypothesized that players who claimed that the amount lost was more than they had expected
were likely to experience a state of cognitive dissonance and would therefore attempt to reduce
their gambling expenditure more than other players who claimed that the amount of money lost
was as much as they expected. Overall, the results contradicted the hypothesis because players
without any cognitive dissonance decreased their gambling expenditure more than players
experiencing cognitive dissonance. However, a more detailed analysis of the data using a
machine-learning algorithm supported the hypothesis because specific playing patterns of six
different gambling groups that were identified explained the paradoxical overall result.
Group 1’s cognitive dissonance is a consequence of their above average losses, the upward
trend of the past six months’theoretical loss, and the fact that they had already self-excluded
from gambling in the past. Group 2 had the highest percentage of females and as noted
previously, females were more likely to experience cognitive dissonance. Group 3 most likely
comprises players who are not entirely honest about their gambling expenditure. They have
the highest losses, prefer playing casino games, and have had voluntary self-exclusions in
the past. Group 4 comprises lottery players whose gambling involvement is very low. It is
therefore understandable that these players are not surprised by the displayed losses and do
not experience cognitive dissonance. Group 5 is very similar to Group 3. However, these
players recently experienced a net win. Therefore, their recent gambling experience (in the
Tab le 6 Behavioral segmentation on players (n= 3783) using a recursive tree algorithm
Group 1 Group 2 Group 3 Group 4 Group 5 Group 6 Total
Response More More About
as much
About
as much
About
as much
About
as much
7-day TL
change
−34% −36% −22% −31% −9% −69% −42%
Loss (in NOK) −769 −730 −1591 −444 3195 −280 −592
Playing days 13 14 17 11 17 5 10
Lottery 29% 31% 27% 51% 14% 55% 43%
Sports betting 19% 1% 14% 7% 24% 5% 9%
VLT 5% 0% 7% 4% 11% 2% 4%
Casino 25% 2% 33% 12% 39% 11% 19%
Scratchcard 13% 59% 5% 12% 4% 20% 16%
Age 324649444839 42
Gender 26% 42% 20% 31% 10% 35% 29%
Past exclusions 30% 9% 40% 13% 43% 12% 22%
TL trend 51% 31% 29% 25% 23% 27% 28%
Playscan rating 62% 100% 100% 0% 84% 22% 48%
N177 162 1051 520 128 1745 3783
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past seven days) was very different from the feedback they received. Their answer is
therefore in line with the data.
Group 6 is similar to Group 4. However, these gamblers play infrequently. Due to the low
gambling involvement, it is likely that these players’gambling expenditure decreases from
one week to the next. The decrease in behavior (reflected by a 69% drop in theoretical loss) is
not so much connected to the personalized information or the answer given, but to their
playing habit. It could easily be the case that that these players did not gamble in the week after
the personalized message was sent as they only gamble five days a month on average. Apart
from Group 6 and Group 4 (who both comprise infrequent lottery players) the most significant
decrease in gambling behavior occurred in Group 1 (34%) and Group 2 (36%). These are the
two groups that are likely to have experienced cognitive dissonance.
Wohl et al. (2017) published the only comparable study to the present study with partic-
ipants who played electronic gaming machines (EGMs). Players who underestimated their
losses (i.e., those who lost more money than they thought) reduced the amount they wagered
and the amount they lost in the subsequent three months. After performing a detailed machine-
learning analysis, the original hypothesis that players who experience cognitive dissonance
decrease their gambling expenditure more significantly than players who do not experience
cognitive dissonance was supported by the data. The overall counterintuitive pattern is more
easily explained when examining the six subgroups of players identified by the learning tree
algorithm. The reasons for the overall paradoxical pattern are likely to be a (i) highly involved
group of casino players who do not appear to experience cognitive dissonance, (ii) highly
involved group of casino players who recently experienced a net win which is not in line with
the feedback which informs them about a loss, and (iii) large group of lottery players who
decrease their gambling, a consequence of their infrequent playing behavior.
In previous studies, personalized feedback has been shown to change player behavior (Auer
and Griffiths 2015a; Auer and Griffiths 2015b; Auer and Griffiths 2016a; Griffiths et al. 2009;
Wood and Wohl 2015). Several studies have also shown that players’subjective loss estima-
tion is significantly biased (Wohl et al. 2017; Auer and Griffiths 2016b) which further
underlines the importance of objective information. The present study utilized the theory of
cognitive dissonance to explain behavioral change as a consequence personalized feedback.
Compared to laboratory studies, the sample size was considerably large and is one of the few
studies to compare objective and subjective data from the same gamblers. However, there are of
course limitations. The players only comprised Norsk Tipping customers who were sampled at
one point in time and were all Norwegian. Although players were asked about their perceived
losses, the results are still inferential rather than definitive. Additionally, behavioral change was
only determined over the period of one week. For that reason, it is difficult to conclude whether
the results can be applied to other areas or platforms. There are a variety of possible avenues for
further research. Future empirical studies should use the underlying methodology outlined in
the present study and be applied to other gambling operators across different countries and
jurisdictions. Gambling behavior should be also assessed over longer periods of time, and the
objective information collected using tracking data should be complemented with self-
recollected information about players’cognitive beliefs and motivations to play. Unlike many
other studies in the area of gambling research, the present study was carried out in a real-world
setting with real players in real time and is one of the very few studies that have directly
compared objective and subjective data from the same gamblers. Given these strengths, the
present study makes a novel and innovative addition to the gambling studies literature.
Int J Ment Health Addiction (2018) 16:631–641 639
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Compliance with Ethical Standards The present study was granted ethical approval by the research team's
university ethics committee. All participants gave their informed consent in participating in the survey.Conflict of
Interest The first author’s company (neccton Ltd.) received funding from Norsk Tipping (the gambling operator
owned by the Norwegian Government) for this work. The second author was sub-contracted by neccton Ltd.The
second author has received funding for a number of research projects in the area of gambling education for young
people, social responsibility in gambling, and gambling treatment from GambleAware (formerly the Responsi-
bility in Gambling Trust), a charitable body which funds its research program based on donations from the
gambling industry. Both authors undertake consultancy for various gaming companies in the area of social
responsibility in gambling.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International
License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and repro-
duction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were made.
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