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ORIGINAL RESEARCH
published: 28 November 2016
doi: 10.3389/fpsyg.2016.01875
Edited by:
Javier Jaen,
Polytechnic University of Valencia,
Spain
Reviewed by:
Luigi Janiri,
Università Cattolica del Sacro Cuore,
Italy
Elena Navarro,
University of Castilla-La Mancha,
Spain
*Correspondence:
Michael M. Auer
m.auer@neccton.com
Specialty section:
This article was submitted to
Human-Media Interaction,
a section of the journal
Frontiers in Psychology
Received: 10 April 2016
Accepted: 14 November 2016
Published: 28 November 2016
Citation:
Auer MM and Griffiths MD (2016)
Personalized Behavioral Feedback
for Online Gamblers: A Real World
Empirical Study.
Front. Psychol. 7:1875.
doi: 10.3389/fpsyg.2016.01875
Personalized Behavioral Feedback
for Online Gamblers: A Real World
Empirical Study
Michael M. Auer1,2*and Mark D. Griffiths1,2
1neccton Ltd, Lienz, Austria, 2Psychology, Nottingham Trent University, Nottingham, UK
Responsible gambling tools (e.g., limit-setting tools, pop-up messages, and
personalized feedback) have become increasingly popular as a way of facilitating players
to gamble in a more responsible manner. However, relatively few studies have evaluated
whether such tools actually work. The present study examined whether the use of
three types of information (i.e., personalized feedback, normative feedback, and/or
a recommendation) could enable players to gamble more responsibly as assessed
using three measures of gambling behavior, i.e., theoretical loss (TL), amount of money
wagered, and gross gaming revenue (GGR) (i.e., net win/loss). By manipulating the
three forms of information, data from six different groups of players were analyzed.
The participant sample drawn from the population were those that had played at least
one game for money on the Norsk Tipping online platform (Instaspill) during April 2015.
A total of 17,452 players were randomly selected from 69,631 players that fulfilled
the selection criteria. Of these, 5,528 players participated in the experiment. Gambling
activity among the control group (who received no personalized feedback, normative
feedback or no recommendation) was also compared with the other five groups
that received information of some kind (personalized feedback, normative feedback
and/or a recommendation). Compared to the control group, all groups that received
some kind of messaging significantly reduced their gambling behavior as assessed
by TL, amount of money wagered, and GGR. The results support the hypothesis
that personalized behavioral feedback can enable behavioral change in gambling but
that normative feedback does not appear change behavior significantly more than
personalized feedback.
Keywords: online gambling, responsible gambling, problem gambling, human–computer interaction, behavioral
feedback, persuasive communication
INTRODUCTION
Gambling is a popular activity in many cultures. Surveys have reported that most people gamble but
do so infrequently (e.g., Wardle et al., 2007). National surveys have also concluded that most people
have engaged in gambling at some point during in their lives (Orford et al., 2003;Meyer et al.,
2009). In Great Britain, the majority of the population (over two-thirds) engaged in at least one
type of gambling in the previous 12 months (Wardle et al., 2012). This included offline and online
gambling. A recent review by Gainsbury (2015) on internet gambling reported that in jurisdictions
that have carried out studies, online gambling prevalence rates are still relatively low (8–16%).
However, as internet gambling is accessible 24 h a day, potentially negative psychosocial impacts
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Auer and Griffiths Personalized Feedback for Online Gamblers
are an almost inevitable consequence for a small minority of
individuals. This is because various structural and situational
characteristics (e.g., accessibility, affordability, and anonymity)
may increase the risk of developing a gambling problem
among vulnerable and susceptible individuals (Griffiths, 2003;
McCormack and Griffiths, 2013). Consequently, players need
to be educated about how to gamble more responsibly and
vulnerable groups need to be protected.
Responsible Gambling and Information
Giving
Responsible gambling tools (e.g., limit-setting tools, pop-up
messages, and personalized feedback) have become increasingly
popular as a way of facilitating players to gamble in a
more responsible manner (Griffiths et al., 2009;Auer and
Griffiths, 2013). They are also given information about common
misconceptions and erroneous perceptions concerning games
of chance as these have been found to be important factors in
the acquisition, development, and maintenance of problematic
gambling (e.g., Gaboury and Ladouceur, 1989;Griffiths, 1994;
McCusker and Gettings, 1997;Parke et al., 2007;Wohl
et al., 2010). However, empirical evaluations demonstrating that
providing gamblers with such information in an attempt to
correct or change erroneous beliefs and misperceptions have
been variable. For instance, some studies have supported the
use of providing information in helping individuals gamble
more responsibly (e.g., Dixon, 2000;Ladouceur and Sevigny,
2003), while other studies have reported no significant association
between providing information and gambling responsibly (e.g.,
Hing, 2003;Focal Research, 2004;Williams and Connolly,
2006).
Responsible Gambling and Correcting
Erroneous Beliefs
Some studies have successfully utilized educational programs as a
way of correcting erroneous beliefs about gambling (e.g., Wulfert
et al., 2006;Wohl et al., 2010). For example, animation-based
educational videos have been developed that educate gamblers
about how slot machines work (Wohl et al., 2010). After watching
the animated video, gamblers said they intended to use strategies
to (i) stay within their limits, and (ii) reduce the number of times
they would exceed their limits. The same study also demonstrated
that animated videos may be an effective tool for increasing the
likelihood of gamblers setting financial spending limits.
Responsible Gambling Messaging and
How it Is Presented
Research has also shown that the way information is presented
can significantly influence behavior and thoughts. Several studies
have investigated the effects of interactive vs. static pop-up
messages during gambling sessions. Static messages do not
appear to be particularly effective, whereas interactive pop-up
messages and animated information have been shown to change
irrational belief patterns and subsequent gambling behavior (e.g.,
Schellink and Schrans, 2002;Ladouceur and Sevigny, 2003;
Cloutier et al., 2006;Monaghan and Blaszczynski, 2007, 2010a;
Monaghan et al., 2009).
It has also been recommended that warning signs containing
information should utilize skills that facilitate self-regulation and
self-appraisal rather than just simply providing information
(Monaghan and Blaszczynski, 2010b). For instance, an
experimental study on slot machine players by Monaghan
and Blaszczynski (2010a) demonstrated pop-up messages that
contained self-appraisal messages resulted in more self-reported
thoughts and behavior while gambling compared to those that
do not.
Responsible Gambling and Pop-Up
Messaging
A study by Stewart and Wohl (2013) found that individuals
were significantly more likely to stick to monetary limits while
gambling if they received a pop-up reminder about monetary
limits compared to those that did not. In a similar study,
Wohl et al. (2013) examined the efficacy of two different
responsible gambling tools (a pop-up message and an educational
animated video) in relation to money limit adherence while
gambling on a slot machine (n=72). The authors reported
that both tools were effective in helping gamblers keep within
their predetermined financial spending limits. Munoz et al.
(2013) conducted a study to examine whether graphic warning
signs had greater efficacy than text-only warning signs. They
reported that the graphic warnings were more successful than
text warnings in getting gamblers to comply with the advice
given, and more successful in getting participants to change their
attitudes concerning gambling. While all of these studies have
provided empirical support for the use of responsible gambling
tools, they are limited by the small sample sizes and the lack of
ecological validity (i.e., the studies were carried out in a laboratory
situation).
More recently, a number of studies have been carried out in
real world settings using real gamblers in real time. For instance,
Auer et al. (2014) investigated the effect of a pop-up message
that appeared after 1,000 consecutive online slot machine games
had been played by individuals during a single gambling session.
The study analyzed 800,000 gambling sessions (400,000 sessions
before the pop-up had been introduced and 200,000 after the
pop-up had been introduced comprising around 50,000 online
gamblers). The study found that the pop-up message had a
limited effect on a small percentage of players. More specifically,
prior to the pop-up message being introduced, five gamblers
ceased playing after 1,000 consecutive spins of the online slot
machine within a single playing session (out of approximately
10,000 playing sessions). Following the introduction of the pop-
up message, 45 gamblers ceased playing after 1,000 consecutive
spins (i.e., a ninefold increase in session cessations). In the latter
case, the number of gamblers ceasing play was less than 1% of the
gamblers who played 1,000 games consecutively.
In a follow-up study, Auer and Griffiths (2015a) argued
that the original pop-up message was very basic and that
re-designing the message using normative feedback and self-
appraisal feedback may increase the efficacy of gamblers ceasing
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Auer and Griffiths Personalized Feedback for Online Gamblers
play. As in the previous study, the new enhanced pop-up
message that appeared within a single session after a gambler
had played 1,000 consecutive slot games. In the follow-
up study, Auer and Griffiths (2015a) examined 1.6 million
playing sessions comprising two conditions [i.e., simple pop-up
message (800,000 slot machine sessions) vs. an enhanced pop-
up message (800,000 slot machine sessions)] with approximately
70,000 online gamblers. The study found that the message
with enhanced content more than doubled the number of
players who ceased playing (1.39% who received the enhanced
pop-up compared to 0.67% who received the simple pop-
up). However, as in Auer et al.’s (2014) previous study,
the enhanced pop-up only influenced a small number of
gamblers to cease playing after a long continuous playing
session. At present, these two research studies (i.e., Auer
et al., 2014;Auer and Griffiths, 2015a) are the only ones
to examine the efficacy of pop-up messaging in a real
world online gambling environment comprising actual online
gamblers.
Responsible Gambling and Personalized
Feedback via Behavioral Tracking Tools
Personalized feedback which informs players about their past
behavior and incorporates a longer time period than just the
current session has only been empirically researched in one
real-world study to date. Auer and Griffiths (2015b) studied
the behavior of 1,015 online gamblers in connection with their
voluntary use of a responsible gaming behavioral tracking tool
compared with 15,216 matched control group gamblers (that had
not used the behavioral tracking tool) on the basis of age, gender,
playing duration, and theoretical loss (TL) [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 showed that online gamblers receiving personalized
feedback spent significantly less money and time gambling in
comparison to those that did not receive personalized feedback
(i.e., the matched controls). However, as gamblers who had used
the behavioral tracking tool had volunteered to use it and had not
been randomly assigned, this meant the effect might not only be
due to the feedback but also to other factors not controllable by
the researchers (for instance, those signing up to use the tool may
have been more responsible gamblers to begin with).
Forsström et al. (2016) carried out a study on the use of the
behavioral tracking tool PlayScan. The data from a total of 9,528
players who voluntarily used the system were analyzed. They
found that the initial usage of the tool was high, but that repeated
usage was low. Two groups of users (i.e., ‘self-testers’ and ‘multi-
function users’) utilized the tool to a much greater extent than
other groups. However, the study did not analyze changes in
behavior as a consequence of using the tool. Wood and Wohl
(2015) obtained data from 779 Svenska Spel online players who
received behavioral feedback using PlayScan. Feedback to players
took the form of a ‘traffic-light’ risk rating that was created via a
proprietary algorithm (red =problematic gambling, yellow =at-
risk gambling, and green =no gambling issues). In addition,
expenditure data (i.e., amounts deposited and gambled) were
collected at three time points (i) the week of PlayScan enrollment,
(ii) the week following PlayScan enrollment, and 24 weeks after
PlayScan enrollment. The findings indicated that those players at-
risk (yellow gamblers) who used PlayScan significantly reduced
the amounts of money both deposited and gambled compared to
those who did not use PlayScan. This effect was also found the
week following PlayScan enrollment as well as the 24-week mark.
Overall, the authors concluded that informing at-risk gamblers
about their gambling behavior appeared to have a desired impact
on their subsequent monetary spending.
Personalized Feedback and Self-Efficacy
The focus of social cognitive theory is self-efficacy and is learned
by observing other individuals’ behavior (Bandura, 2001). Self-
efficacy primarily concerns how capable an individual feels
about performing a behavior and is at the heart of the health
communication literature including the Health Belief Model
(Maiman and Becker, 1974;Janz and Becker, 1984), Theory of
Planned Behavior (Ajzen, 1985), Protection Motivation Theory
(Rogers, 1983), and the Extended Parallel Process Model (Witte,
1992). All these theoretical perspectives assert that high levels
of self-efficacy are highly likely to enable behavioral change.
Consequently, it is important that to enable behavioral change,
messages must include components of self-efficacy (i.e., belief
that the person can carry out an action) and response efficacy (i.e.,
belief that the recommended action will lead to a desired outcome
for the person; Witte et al., 2001;Perloff, 2008).
Another method of attempting to enable behavioral change
in gambling is normative feedback. Studies researching smoking
(Van den Putte et al., 2009), condom use (Yzer et al., 2000),
and marijuana consumption (Yzer et al., 2007) have shown
normative beliefs can play an important role in behavioral
change. In a study of American college student gambling, Celio
and Lisman (2014) demonstrated that personalized normative
feedback decreased other students’ perceptions of gambling
and lowered risk-taking performance on two analog measures
of gambling. They concluded that a standalone personalized
normative feedback intervention may modify gambling behavior
among college students. Miller and Rollnick (1991) have
also emphasized that normative feedback is important in
facilitating behavioral change in the use of motivational
interviewing.
Outside of the gambling studies field, personalized behavioral
feedback has been used to change other potentially addictive
behaviors (e.g., cigarette smoking). Using a combination of
both motivational interviewing and feedback from ultrasound
was found as effective for reducing cigarette smoking among
pregnant women (Stotts et al., 2009). Another study effectively
delivered a smoking-cessation intervention via wireless text
messages to college students using integrated internet/mobile
phone technology (Obermayer et al., 2004). Another area where
behavioral feedback has been investigated is in the area of sports
and fitness. Buttussi et al. (2006) investigated the use of mobile
phone guides in fitness activities using a Mobile Personal Trainer
(MOPET) application. The mobile app gave verbal navigation
assistance and also used a 3D-animated motivator. Evaluation of
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the results supported the use of mobile apps and embodied virtual
trainers in outdoor fitness applications.
Many of the aforementioned approaches enabling behavioral
change utilize the ‘stages of change’ model (Prochaska and
DiClemente, 1983;Prochaska and Prochaska, 1991) and
motivational interviewing (Miller and Rollnick, 1991). Based on
these theoretical approaches, the present authors believe that
to change an individual’s gambling behavior, player feedback
(utilizing behavioral tracking data) has to incorporate the
stages of change model and be presented in a motivational
way. More specifically, this means providing informational
feedback to gamblers in a non-judgmental format along with
normative information so that they can evaluate their gambling
behavior compared to others like themselves. Transparent and
non-judgmental feedback is important and has been emphasized
by studies elsewhere. For instance, a study examining alcohol
drinking by Lapham et al. (2012) advocated that feedback should
be transparent and tailored to the individual (e.g., the extent to
which individuals exceeded daily or weekly alcohol limits). This
is because their participants explicitly wanted to know how they
came to be specifically assigned to their alcohol drinking risk
category.
Human–Computer Interaction and
Persuasive System Design in
Responsible Gambling
Given that the primary aim of gambling pre-commitment tools is
to enable behavioral change, it is only recently that designs of such
tools have utilized the principles of human–computer interaction
(HCI) and persuasive system design (PSD). A recent study found
that a monetary limit pop-up tool inspired by PSD and HCI
principles was much more effective than tools not incorporating
such principles (Wohl et al., 2014). As a research field, HCI
examines the interaction of individuals with technology and
attempts to facilitate usability and uptake. Persuasive Technology
has been defined as interactive computing systems that attempt
to change people’s attitudes and behaviors (Fogg, 2003). Apart
from user-feedback, HCI principles relevant for the design of
pre-commitment measures are an aesthetic visual design, the
incorporation of system-status updates, a sense of control over
functionality, and the use of simple language (Hewett et al.,
1992;Shneiderman et al., 2009;Preece et al., 2011). Apart from
showing that messaging can effectively change thoughts about
gambling and the gambling behavior itself, research has also
suggested that the content of messages is important (Monaghan
and Blaszczynski, 2010a,b;Auer and Griffiths, 2015b). The
present authors take the view that the design of a feedback
system for facilitating responsible gambling is paramount, and
that HCI and PSD principles should be at the heart of such
systems.
The Present Study and Hypotheses
This study goes beyond previous research as it applies an
experimental approach in a real-world online gambling setting.
This is in contrast to Auer and Griffiths’ (2015b) study in
which players voluntarily signed up for a service that provided
them with personalized spending information. In this study,
players were randomly assigned to different types of interventions
in order to investigate the effects of personalized feedback,
normative feedback, and non-personalized recommendations.
Additionally, a control group was drawn that allows for the
causal inference of the different types of interventions. The
main research questions (RQs) were: (RQ1) Does personalized
information given to gamblers reduce their gambling behavior?
(RQ2) Do different types of personalized information (i.e.,
personalized feedback, normative feedback, a recommendation
to gamble responsibly) given to gamblers reduce their gambling
behavior in different ways? (RQ3) Are gamblers’ demographics
and playing attributes associated with their gambling behavior
in reaction to the various messaging interventions or do all
gamblers react similarly to the specific interventions, regardless
of the message attributes? It was hypothesized that compared to
the control group, personalized feedback would impact positively
on subsequent playing behavior as assessed by a reduction in
time and money spent in the experimental groups (H1), and
that the impact of personalized feedback and normative feedback
would be larger compared to either a pure recommendation or
no information at all (H2).
MATERIALS AND METHODS
Participants
The participant sample drawn from the population were those
that had played at least one game for money on the Norsk Tipping
online platform (Instaspill) during April 2015. A total of 17,452
players were randomly selected from 69,631 players that fulfilled
the selection criteria (see next section for Sampling procedure).
Ten players had won more money than they wagered over the
time period and were thus excluded from analysis leaving 17,442
participants. Of these, 12,261 were males (69.1%) and 5,481 were
females (30.9%). All but 40 participants were Norwegian. The
mean average age was 40.52 years (SD =13.19). Approximately
29% of the customers were younger than 30 years, and 22%
were aged over 50 years. There is no significant age difference
between males and females. Participants had been playing with
Norsk Tipping for a mean average of 94 months (7.9 years;
SD =38.31).
Sampling
The participants only comprised players who had a net loss across
all games in the past month before the study commenced (i.e.,
winners were excluded). Those who had self-excluded and/or
taken a play break from gambling were also excluded from
subsequent analysis. More specifically, the sample was drawn
based on the amount lost across all games (online casino, sports
betting, lottery, etc.) apart from scratchcards purchased offline
(as these data were not fully available during the study period).
The amount of money lost by each player was computed by
simply subtracting the amount wagered from the amount won.
The overwhelming majority of players lost only small amounts of
money. Therefore, in order to examine the impact of messaging
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on high intensity players, there was an oversampling of high
intensity gamblers.
Experimental Design
The study examined the effects of personalized messaging and
manipulated the information given. Table 1 contains the different
permutations for the experimental design. The six groups were
created based on three types of messaging intervention. These
were:
1. Personalized information about the participant’s gambling
behavior (in the form of numbers and illustrations).
2. A recommendation (in the form of written information about
using responsible gaming tools offered by Norsk Tipping).
3. Normative feedback (in the form of numbers and illustrations
displaying the gambling intensity of the average active player
at Norsk Tipping compared to their own).
Group 5 was designed to evaluate the effectiveness of non-
personalized, purely informative recommendation. Group 6
served as the control group as participants did not receive
any information at all. The distribution of high and low
intensity players created by the sampling procedure was the
same across all six groups. Each group contained approximately
2,957 participants. In this 2 ×2×2 design, only six groups
were examined (rather than the normal eight) because normative
feedback without personalized feedback would not make any
theoretical sense. This is also the case with providing normative
feedback and recommendations without personalized feedback.
The study was planned over the course of 1 year and
executed during May and June, 2015. During the course
of the study, players (excluding the control group) received
information about their losses over the past 6-month period
and/or, recommendations about existing responsible gaming
tools, and/or normative information. It was hypothesized that
personalized behavioral feedback would help players to gamble
more responsibly (as assessed using the TL, the amount of money
wagered in Norwegian Krone [NOK], and the gross gaming
revenue (GGR) [NOK] – see ‘Analysis’ section below for more
detail).
All players that received information were told (in writing)
that Norsk Tipping was trying out new services and that the
operator would be very grateful if they could participate in the
study. Players were told that they would be receiving information
that would help them become more aware of their personal
gambling expenditure. They were also informed that a research
TABLE 1 | Experimental groups by type of feedback provided to gamblers.
Personalized
information
Recommendation Normative
feedback
Group 1 YES NO NO
Group 2 YES YES NO
Group 3 YES YES YES
Group 4 YES NO YES
Group 5 NO YES NO
Group 6 NO NO NO
team had been asked to evaluate the effects of this service
and that only anonymized data would be used for research
purposes. Players had to press a ‘Next’ button to confirm that
they agreed to participate and that their data would be used for
research purposes. To not be included in the study, players were
informed that they could simply close the message window. The
demographic distribution was the same for all six groups (i.e.,
there were no significant differences across the six groups in
terms of gender, age, and nationality). Of the 17,452 randomly
selected players, 5,528 voluntarily participated in the study.
Personalized Feedback
A simple personalized message was sent to players in Groups 1–4
that said: “How much do you think you spent on gambling
recently? Our records show that you lost [X] NOK last month.”
In addition, players were also presented with a line chart (see
Figure 1) that contained the monthly values for their personal
losses over the previous 6-month period. Players were also told
that they could retrieve the information any time during the
following month.
Normative Feedback
A simple message with normative feedback was sent to players
in Groups 3 and 4 that said: “It can be helpful to know about
other peoples’ expenditures to evaluate your own spending. For this
reason we would like to let you know that the average Norsk Tipping
player loses about 400 NOK per month.” The normative feedback
about other players’ losses was provided after the personalized
feedback. Additionally, a line chart (see Figure 2) displaying
their own losses compared with those of other players was also
provided.
Recommendation
Groups 2, 3, and 5 received a helpful recommendation about
responsible gambling tools and services that players could access
via a hyperlink on the screen. Players could access tools provided
by Norsk Tipping that helped players (i) manage their personal
spending limits, (ii) activate a play break, (iii) take a diagnostic
self-test about their gambling behavior, and (iv) see an overview
of their recent spending. Players were also informed about the
national gambling helpline if they wanted to speak to anyone
about their gambling.
Analysis
Assessing whether personalized feedback results in the desired
behavioral change means that behavioral change has to be
assessed via specific variables and via specific time periods. In the
present study it was decided that gambling behavior 7 days prior
to the intervention would be compared with gambling behavior
7 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.
The three measures of behavior that were used to assess
gambling behavior were TL, amount of money wagered, and GGR
(i.e., net win/loss). The TL statistic was computed as ‘TL_after’
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FIGURE 1 | Line chart containing personalized feedback of money lost (in NOK).
FIGURE 2 | Line chart containing personalized feedback of money lost (in NOK) compared to other players’ losses (i.e., normative feedback).
minus ‘TL_before’ divided by ‘TL_before.’ This statistic reflects
the change in behavior 7 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 7 days before. The statistic ranges from −1
to +infinity. A value of −1 means that the player did not
engage in gambling 7 days after the message was read. A value
of +10 means that the gambling behavior increased 1000% in
the 7 days after the message was read compared to 7 days
before the message was read. Prior to analysis, data cleaning was
performed. More specifically, it appeared that a few customers
(n=28) had negative TL values that led to negative change
metrics. Consequently, these 28 customers were removed from
the analysis. An outlier procedure was also performed which
removed the 1% highest values in the change metric as this
was naturally highly skewed. The comparisons across the groups
were performed via Mann–Whitney UTests due to the skewed
distribution of the metrics. Three statistical tests were carried out
using chi-square analysis and using a Bonferroni correction, the
significance level was determined to be 0.0167. The study was
granted ethical approval by the research team’s University Ethics
Committee.
RESULTS
Theoretical Loss Analysis
Table 2 displays the statistics of the TL change variable after
data cleaning. The largest values (top 1%) were discarded
TABLE 2 | Parametric (e.g., mean) and non-parametric (e.g., 1st quantile,
median) statistics of the cleansed theoretical loss over a 7-day period.
Minimum −1.00
1st Quantile −0.87
Median −0.42
Mean 0.06
3rd Quantile 0.15
Maximum 16.00
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from further analyses as these extreme outliers would influence
the results heavily. Very large values occur if players were
gambling little before the intervention and heavily after as a
consequence of the quotient which is computed. It can be
safely assumed that those players who spend very little and
received feedback are not the main target group for such
interventions. The arithmetic mean demonstrated that the
average player increased their gambling by about 6%. However,
it is evident that the distribution is highly skewed as the median
is much smaller. Half of the players (50%) decreased their
gambling by about 42%. The non-normal distribution is of
course due to the fact that there is a lower limit at −1 and
a much higher limit at 16 (i.e., the maximum increase was
1600%).
Control Group Analysis
Players in the control group did not have a response date as
there were no messages or recommendation sent to them. Players
who received a message and read it had a specific response
date on which they read the message. In order to do that they
had to be online and be actively gambling. Given the missing
response date in the control group, it is not easy to compare
the computed change in gambling behavior 7 days after the
message was read to a corresponding change in the control group.
Simply looking at the gambling behavior in the same month
as the other players received personalized messages is not valid
as the responders in Groups 1–5 naturally showed some sort
of activity and no such selection can be applied in the control
group. Consequently, the level of monthly gambling across the
responders is naturally higher than that of the control group.
Therefore, an alternative procedure was chosen to analyze the
data. In order to determine if the changes observed in Groups
1–5 were due to the message, it is important to find out how
much players change on average when they do not receive a
personalized message.
For each of the 2,958 control group members, every day on
which they showed gambling activity was selected. This could
be 1 day, a few days, or every day. For each and every control
group player, a value between 0 and 30 with respect to active
gambling days was calculated. For each and every player, and
every active playing day, the gambling activity 7 days before that
day and 7 days after that day was computed. Finally, the change
statistic as used above was computed. Overall, it was assumed that
all influences cancel each other out and that the average change
statistic (across players and days) yields an average change in
behavior.
Message Effect Analysis
Table 3 shows the distribution of the change in TL across five
experimental groups and the control group. The control group
statistics were computed across control group members and
the respective days on which they were active. Examination
of the median, mean, and maximum value clearly shows
that the control group changed their gambling behavior less
than the experimental groups. Whereas half of the players
decreased their intensity by at least 42% (median) in the
target groups, it was only 36% (median) in the control group.
TABLE 3 | Parametric (e.g., mean) and non-parametric (e.g., median, 1st
quantile) statistics of the theoretical loss change variable by group
mapping over a 7-day period).
Target group Control group
Minimum −1−1
1st Quantile −0.87 −0.87
Median −0.42 −0.36
Mean 0.06 0.58
3rd Quantile 0.15 0.37
Maximum 16.0 46.4
The average player (arithmetic mean) increased their play
by 6% in the target group and 58% in the control group.
Using a Mann–Whitney UTest, comparison of TLs after
7 days of receiving personalized messages between Groups
1 to 5 and the control group (Group 6) was significant
(X2=32.208, df =5, p<0.0001). Thus, personalized
feedback appears to have had a significant impact on player
behavior compared to those that had no such feedback (see
Table 4).
Figure 3 shows the change in TL across all six groups, 7 days
after the message was read. Again, this shows that the control
group (Group 6) decreased the least in relation to TL (−36%)
and that Group 2 had the largest decrease in play in relation to
TL (−45%).
Data also showed that the most gambling-intense players
often deplete their financial resources during the 1st week of
the month. During this time they reach their spending limits
and can only resume playing at the site at the beginning of the
next month. For that reason, a further analysis was carried out
on those players who responded to the messaging during the
1st week of the study. Figure 4 (using TL) shows the change
in behavior 7 days after the message was read for players who
responded during the 1st week of the study. It is again evident
that using the TL, players who did not receive any message
and players who only received purely informational content
decreased their play less than players who received personalized
feedback.
It is also important to examine whether the control group
changed their gambling behavior with respect to the GGR
and the amount of money wagered. The median change in
amount wagered over a period of 7 days was −34%, and
is a smaller decrease compared to the experimental Groups
1–5 (see Table 4), and was significant (X2=26.66, df =5,
TABLE 4 | Differences between the experimental groups (EGs) and control
group (CG) 7 days after gamblers had received personalized messages
about their gambling behavior
CG EGs X2d.f. p
Theoretical loss −36% −42% 32.208 5 0.0001∗
Amount wagered −34% −43% 26.66 5 0.0001∗
Gross gaming −48% −58% 28.66 5 0.0001∗
∗Bonferroni correction significance level =0.0167.
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FIGURE 3 | Median 7-day change of theoretical loss variable by experimental group.
FIGURE 4 | Median 7-day change of theoretical loss variable by experimental group for 1st-week responders.
p<0.0001). The median change in GGR over a period of
7 days was −48%, and is a smaller decrease compared to the
experimental Groups 1–5 (see Table 4), and was also significant
(X2=28.66, df =5, p<0.0001).
Sub-Group Analysis by Game Type and
Demographics
The previous section demonstrated that players who received
personalized feedback changed more than those who only
received a recommendation and more than those who did
not receive any kind of information. For this reason, further
analysis investigated whether gamblers’ demographics and
playing attributes were associated with their gambling behavior
in reaction to the various messaging interventions. However,
separating out all players into different clusters is difficult. In
cluster analysis, statistical methods identify segments that are
different from each other whereas the members of a single
segment are similar to each other.
In this case, the goal was slightly different. If the study
population was randomly divided into clusters, each cluster
would be made up of some players who received personalized
feedback, some players who received normative feedback, etc.
However, the clustering should occur according to a specific goal.
Here, members of a specific cluster that received personalized
feedback should be different with respect to the behavioral change
from members of that cluster who received normative feedback,
etc. This means that an algorithm should uncover different
clusters, whereas each cluster is characterized by a maximum
variance across the five different interventions with respect to
behavioral change.
In order to delineate groupings that have a unique profile with
respect to the five experimental groups, a two-step approach was
chosen. This consisted of a k-means statistical procedure and
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a genetic algorithm. Genetic algorithms are machine-learning
procedures that imitate evolutionary processes. Parameters are
randomly chosen and are optimized due to the performance
of the algorithm that is measured via a fitness function. In
this case, the fitness function was defined via the profiles of
the clusters that the k-means produced. The more distinct the
members of a cluster with respect to the five experimental
interventions, the higher the corresponding fitness function. The
open source statistical package ‘R’ was used to analyze the data
and perform the genetic algorithm. (Details about the procedure
and the parameters used in the genetic algorithm are available
from the first author). Finally different types of players were
identified based on this two-step approach (all distinct with
respect to their characteristics as well as the reaction toward
the different messaging interventions). This led to five distinct
groups of player whose profile is displayed in Table 5. The
following descriptions are an attempt to capture the main aspects
of the five profiles of players in relation to game preference and
GGR: Profile 1 – Female scratchcard players with average GGR;
Profile 2 – Lottery players with a low GGR; Profile 3 – At-
risk lottery players with higher GGR; Profile 4 – At-risk casino
players with past self-exclusions and a high GGR; Profile 5 – At-
risk male sports bettors who recently won with a below average
GGR.
The numbers in Table 6 report the median 7-day change with
respect to the theoretical loss in the five experimental groups for
each player profile. For instance, players clustered in Profile 1
who were subject to Intervention 1 (personalized feedback only)
reduced their gambling on average by 68% 7 days after they read
the message. Players clustered in Profile 3 who were subject to
Intervention 3 (personalized feedback, normative feedback, and
a recommendation) reduced their gambling on average by 29%
7 days after they read the message. More specifically, the analysis
demonstrated that:
•Profile 1: This group of players showed the largest decrease
in play when presented with personalized feedback and a
recommendation (i.e., a 68% reduction). The smallest decrease
in play was when personalized and normative feedback were
given together.
•Profile 2: This group of players showed the largest decrease
in play when all three types of messaging were combined
(personalized and normative feedback along with a
recommendation). The smallest decrease in play was when
they were given a recommendation only.
•Profile 3: This group of players showed the largest decrease
in play when they were given personalized feedback and a
recommendation or personalized and normative feedback.
The smallest decrease in play was when they were given a
recommendation only.
•Profile 4: This group of players showed the largest decrease
in play when they were given a recommendation only.
The smallest decrease in play was when they were given
personalized feedback only.
•Profile 5: This group of players showed the largest decrease
in play when they were given personalized feedback only.
The combined intervention of personalized and normative
feedback, and a recommendation appeared to increase play.
As shown in Table 6, in four of the five profiles, personalized
feedback had a larger effect on gambling behavior (TL) than a
sole recommendation. It was only in Profile 4 (highly involved
at-risk casino players) where this did not hold true. Here, a sole
recommendation had the largest effect on subsequent gambling
behavior. The findings also suggest that although sports bettors
benefit from personalized feedback, normative feedback plus a
recommendation does not appear to make this group of players
gamble less.
TABLE 5 | Profile of five groups that reacted differently toward messaging interventions.
Player group Profile 1 Profile 2 Profile 3 Profile 4 Profile 5 Average
Number of participants 348 1,940 1,208 1,010 404 4,910
GGR 430 293 673 738 370 494
Total amount wagered (NOK) 732 634 1,552 16,839 3,926 4,471
Number of playing days 8 7 13 15 15 11
Other games 2% 3% 14% 1% 2% 5%
Lottery 21% 67% 50% 7% 13% 43%
Online casino 75% 23% 23% 75% 11% 36%
Sports betting 1% 4% 7% 5% 56% 9%
Sport 0% 1% 3% 1% 15% 3%
Slots (land-based) 0% 2% 3% 12% 2% 4%
Casino (land-based) 2% 7% 6% 67% 5% 19%
Scratchcards 73% 16% 16% 2% 5% 16%
Bingo 0% 0% 1% 6% 1% 2%
Mean age (years) 39 37 48 46 41 42
Tenure 94 93 113 106 100 101
Gender (female) 47% 33% 30% 24% 5% 29%
Self-exclusion 8% 7% 16% 62% 23% 22%
PlayScan risk 49% 13% 75% 79% 64% 49%
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TABLE 6 | The effect of the different messaging interventions in each of the
five experimental groups with respect to the change in theoretical loss.
Profile 1 Profile 2 Profile 3 Profile 4 Profile 5
Intervention 1 −68% −56% −31% −16% −41%
Intervention 2 −87% −53% −35% −27% −21%
Intervention 3 −57% −67% −29% −19% 18%
Intervention 4 0% −57% −35% −22% −17%
Intervention 5 −68% −48% −20% −35% −27%
DISCUSSION
The present study examined whether the use of three types of
information (i.e., personalized feedback, normative feedback, and
a recommendation to gamble more responsibly) could enable
players to gamble more responsibly as assessed using three
measures of behavior, i.e., TL, amount of money wagered, and
GGR. By manipulating the three forms of information, data
from six different groups of players were analyzed. The first
hypothesis was that compared to the control group, personalized
feedback would impact positively on subsequent playing behavior
as assessed by a reduction in time and money spent in the
experimental groups in terms of TL, amount of money wagered
and GGR. On the whole, empirical support for H1 was confirmed.
The second hypothesis was that that the impact of personalized
feedback and normative feedback would be larger compared
to either a pure recommendation or no information at all.
H2 was also empirically supported although the difference in
reduction of gambling behavior by viewing a recommendation
only (compared to personalized and normative feedback) did not
quite reach statistical significance.
The first research question (RQ1) the present study attempted
to answer was whether personalized feedback given to gamblers
reduces their gambling behavior? The findings presented here
suggest that it does and supports previous real world studies
showing that personalized feedback about gambling behavior
can help some players to decrease the amount of time and/or
money they spend gambling (Auer et al., 2014;Auer and Griffiths,
2015a,b). However, almost all previously published research that
has examined player feedback to date has been conducted in
laboratory settings and has not looked beyond a single gambling
session. The present study is a significant advance on those
studies as it was carried out in the real world and for a period
longer than within a single gambling session. Given this, the
present study makes both a novel and original addition to the
literature.
The second research question (RQ2) was to investigate
whether different types of personalized feedback given to
gamblers reduce their gambling behavior in different ways?
Players that received both personalized feedback and a
recommendation (Group 2) decreased gambling behavior
the most although this was not statistically significant compared
to the other four experimental groups. Those players given a
recommendation only (Group 5) had the least change in their
gambling behavior but again this did not quite reach statistical
significance compared to the other four experimental groups.
The other three groups (1, 3, and 4) showed equivalent decreases
in gambling behavior. Findings suggest that the additional
normative feedback received by Group 3 (who received all three
types of message) did not have a more significant impact on
gambling behavior than other groups. This may be due to a
number of reasons. It could be due to the amount of information
given (information overload – too many messages), or the
fact that the types of gambler were diverse and that different
messaging may impact more on different types of gambler.
Furthermore, only one specific type of normative feedback was
included in the present study (i.e., a comparison with the average
player).
In relation to changes in GGR 7 days before and 7 days after
the information was provided to players, gambling decreased
more in Groups 1–4 (all of who received some kind of
personalized feedback) compared to Group 5 (who received a
recommendation only) supporting H1, although this did not
quite reach statistical significance. This again tentatively supports
the hypothesis that personalized feedback has a stronger effect
in changing gambling behavior (as assessed using GGR) than
pure informational content. In relation to changes in GGR
30 days before and 30 days after the information was provided,
the findings were similar. Those groups that received some
kind of personalized feedback (Groups 1–4) decreased their
play (as assessed using GGR) after the message was provided.
Those players that received a pure recommendation (Group 5)
increased their play. Although across the groups as a whole
the differences were not statistically significant, they are in line
with H2.
In addition to TL and GGR, other studies (e.g., Broda
et al., 2008;LaBrie et al., 2008;LaPlante et al., 2008, 2009;
Nelson et al., 2008;Dragicevic et al., 2011;Braverman and
Shaffer, 2012;Gray et al., 2012;Braverman et al., 2013) have
frequently used the amount of money wagered as a proxy
measure for gambling intensity. As with the findings above,
the amount wagered by players 7 days after they had received
personalized information was significantly less in the control
group (supporting H1), and also less in players who only
received a recommendation (supporting H2, although the finding
was not statistically significant in the group that only received
a recommendation compared to the other four experimental
groups). After a period of 30 days, all groups tended to increase
their play (as assessed using the amount wagered) after the
information had been provided. Those that only received a
recommendation (i.e., Group 5) showed the highest increase in
amount wagered compared to the other four experimental group
(supporting H2), although there was no significant difference
across the five groups as a whole. Taken as a whole in relation
to TL, GGR and amount wagered, the findings indicate that
behavioral change is more intense in the days immediately after
the information is provided to players, and also suggests that such
information should be given more regularly.
The third research question (RQ3) investigated whether
gamblers’ demographics and playing attributes are associated
with their gambling behavior in reaction to the various messaging
interventions or whether all gamblers react similarly to the
specific interventions, regardless of the message attributes? To
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Auer and Griffiths Personalized Feedback for Online Gamblers
supplement and refine the analysis in relation to RQ3, different
types of player were delineated using a k-means procedure and
which led to five different player profiles based on gender,
game-type, and GGR. These were: (i) female scratchcard players
with average GGR, (ii) lottery players with a low GGR, (iii)
at-risk lottery players with higher GGR, (iv) at-risk casino
players with past exclusions and a high GGR, and (v) at-risk
male sports bettors who recently won with a below average
GGR. Results showed that in all profiles (bar the at-risk casino
players), personalized feedback had a larger effect on gambling
behavior than providing a sole recommendation. Providing
a sole recommendation to at-risk casino players led to the
most significant reduction in gambling behavior. Such findings,
although arguably tentative, suggest that operators will need to
use their data to more specifically target specific types of players.
However, the findings presented here need to be replicated
using other datasets from other operators before more general
recommendations can be made to online gamblers as a whole.
Based on these findings, normative feedback might have
worked better if it had been tailored to the type of player
(e.g., giving sports bettors normative information about other
sports bettors rather than gamblers in general) and therefore
perceived as more relevant for the recipient. The comparison of
TL data 30 days before compared to 30 days after the information
was provided showed a slight increase in gambling behavior.
However, the smallest increase in gambling was amongst those
receiving personalized feedback and a recommendation (Group
2) and the largest increase in gambling behavior occur was among
those receiving a recommendation only (i.e., Group 5).
Limitations
Unlike the vast majority of studies carried out in the gambling
studies field, the present study was a real world study using
real online gamblers and carried out in real time. Furthermore,
the sample size was relatively large and the dataset was robust.
However, the study is not without its limitations. The players only
comprised those that had gambled on the Norsk Tipping online
platform during 1 month in 2015 and only evaluated the efficacy
of viewing a single message on short-term behavior change in
gambling (i.e., 1 week and 1 month after the message was viewed).
Generalizations to other types of online gambler either in Norway
or other countries cannot be assumed. Furthermore, players
selectively retrieved the message and the information remained
static, although the player behavior changed. It was also difficult
to compare the players to the control group due to the fact that
message retrieval was voluntary. Additionally it is not known if
players read the message once or several times. Furthermore, the
present study did not vary the display of the message content in
terms of color or wording which may also have had a considerable
impact on the message effect.
Future Research
Based on the findings presented here, there are many possible
avenues for further research. Such research could investigate (i)
the use of personalized messages more than once (for instance,
the showing of messages once every month), (ii) the effects of
recent wins on playing behavior and to what extent personalized
messaging helps or hinders further gambling behavior, (iii)
secondary data analysis of behavior connected to the retrieval
of personalized spending information, (iv) varying the message
content (e.g., emotional vs. warning vs. informative), (v)
personalized messaging that addresses specific types of behavior
(binge gambling, high loss gambling, high win gambling, etc.),
(vi) the effects of regular personalized feedback compared to
one-time feedback (e.g., weekly vs. monthly feedback), and (vii)
long-term gambling behavior of samples rather than just over a
1-month period.
CONCLUSION
A number of conclusions based on the findings in the present
study can be made. First, personalized behavioral feedback can
enable behavioral change in gambling (based on data comparing
those who received such messaging compared to those who did
not). Second, additional normative feedback does not appear
change behavior significantly more than personalized behavioral
feedback. Third, patterns of gambling behavior associated with
the effects of personalized messaging can be derived. Fourth,
messages are most likely read during the 1st week after players
have received them. Fifth, lottery players and female scratchcard
players are more likely to read the message and act on messages
than casino players. Sixth, lottery players (low and high spending)
appear to benefit most from personalized feedback. Seventh,
normative feedback does not appear to be beneficial for sports
bettors, and high spending casino players do not appear to
benefit from personalized feedback. In short, the data show that
the effect of the three types of messaging (i.e., personalized
feedback, normative feedback, and a recommendation) appears
to depend upon players’ gambling habits as well as demographic
and game-type factors. This is especially important in practical
terms because operators will also need to take into account
player attributes and behaviors when presenting different kinds
of messages. The present study demonstrates one way in which
operators could use the big data that they routinely collect
to help inform and encourage responsible gambling among
its clientele. The study also demonstrates the positive way
in which academic researchers can collaborate in innovative
research initiatives that ultimately help relevant stakeholder
groups.
AUTHOR CONTRIBUTIONS
MA: Co-designed the study, analyzed the data, and contributed to
writing of the paper. MG: Co-designed the study and contributed
to writing the paper.
FUNDING
This study was funded by Norsk Tipping. The second author was
subcontracted by neccton Ltd.
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Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Norsk Tipping provided access to the data and assisted in interpreting the results.
However the writing of the paper, interpretation and conclusions reached were
written in an independent capacity and not influenced.
Copyright © 2016 Auer and Griffiths. This is an open-access article distributed
under the terms of the Creative Commons Attribution License (CC BY). The
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original author(s) or licensor are credited and that the original publication in
this journal is cited, in accordance with accepted academic practice. No use,
distribution or reproduction is permitted which does not comply with these
terms.
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