The Impact of Personalized Feedback Interventions by a Gambling Operator on Subsequent Gambling Expenditure in a Sample of Dutch Online Gamblers

Abstract

Player protection has become an important area for the gambling industry over the past decade. A number of gambling regulators now require gambling operators to interact with customers if they suspect they are gambling in a problematic way. The present study provided insight on the impact of personalized feedback interventions (PFIs) on subsequent gambling behavior among a Dutch sample of real-world gamblers. Nederlandse Loterij (the national Dutch Lottery operator) provided access to a secondary dataset comprising tracking data from online casino and sports betting gamblers (N = 2,576) who were contacted either by e-mail or telephone between November 2021 and March 2022 if they showed signs of problematic gambling as identified using behavioral tracking software. Compared to matched controls (n = 369,961 gamblers), Dutch gamblers who received a PFI (via e-mail [n = 1876] or a telephone call [n = 700]) from the gambling operator had a significant reduction in amount of money deposited, amount of money wagered, number of monetary deposits, and time spent gambling in the 30 days after being contacted. Gambling frequency as measured by the number of gambling days did not change significantly after a PFI. Telephone calls did not lead to a significant larger reduction with respect to the aforementioned behavioral metrics. High-intensity players reduced their gambling behavior as frequently as low-intensity players, which means that the intervention’s success was independent of gambling intensity. The impact on subsequent gambling was the same across age groups and gender. The results of the present study are of use to many different stakeholder groups including researchers in the gambling studies field and the gambling industry as well as regulators and policymakers who can recommend or enforce that gambling operators utilize responsible gambling tools such as using PFIs to those who may be displaying problematic gambling behaviors as a way of minimizing harm and protecting gamblers.

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ORIGINAL PAPER
Accepted: 25 September 2022
© The Author(s) 2022
Extended author information available on the last page of the article
The Impact of Personalized Feedback Interventions by a
Gambling Operator on Subsequent Gambling Expenditure in
a Sample of Dutch Online Gamblers
MichaelAuer1· Mark D.Griths2
Journal of Gambling Studies
https://doi.org/10.1007/s10899-022-10162-2
Abstract
Player protection has become an important area for the gambling industry over the past
decade. A number of gambling regulators now require gambling operators to interact with
customers if they suspect they are gambling in a problematic way. The present study pro-
vided insight on the impact of personalized feedback interventions (PFIs) on subsequent
gambling behavior among a Dutch sample of real-world gamblers. Nederlandse Loterij
(the national Dutch Lottery operator) provided access to a secondary dataset compris-
ing tracking data from online casino and sports betting gamblers (N = 2,576) who were
contacted either by e-mail or telephone between November 2021 and March 2022 if they
showed signs of problematic gambling as identied using behavioral tracking software.
Compared to matched controls (n = 369,961 gamblers), Dutch gamblers who received a
PFI (via e-mail [n = 1876] or a telephone call [n = 700]) from the gambling operator had a
signicant reduction in amount of money deposited, amount of money wagered, number
of monetary deposits, and time spent gambling in the 30 days after being contacted. Gam-
bling frequency as measured by the number of gambling days did not change signicantly
after a PFI. Telephone calls did not lead to a signicant larger reduction with respect
to the aforementioned behavioral metrics. High-intensity players reduced their gambling
behavior as frequently as low-intensity players, which means that the intervention’s suc-
cess was independent of gambling intensity. The impact on subsequent gambling was the
same across age groups and gender. The results of the present study are of use to many
dierent stakeholder groups including researchers in the gambling studies eld and the
gambling industry as well as regulators and policymakers who can recommend or enforce
that gambling operators utilize responsible gambling tools such as using PFIs to those
who may be displaying problematic gambling behaviors as a way of minimizing harm
and protecting gamblers.
Keywords gambling · online gambling · problem gambling · The Netherlands ·
gambling operator interactions · gambling expenditure
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Journal of Gambling Studies
Introduction
Online Gambling and Problem Gambling
Gambling is an activity in which individuals stake money (or something of nancial value)
on an event in which the outcome is unknown in an attempt to gain more money (or some-
thing of greater nancial value) (Griths, 1995). Individuals can gamble in oine brick-
and-mortar establishments such as casinos, gambling halls, amusement arcades and betting
shops or they can gamble online. Online gambling products are usually similar to land-
based products and the only dierence is the mode of access (Gainsbury, 2015). A number
of previous studies have underlined the elevated risk of online gambling (e.g., Griths et
al. 2006; Hubert & Griths 2018; McBride & Derevensky, 2009; McCormack et al., 2014).
Griths (2003) posited a number of situational factors in relation to online gambling that
could lead to vulnerable individuals having increased gambling problems. Among the fac-
tors mentioned were high accessibility, anonymity, convenience, escape, immersion/disso-
ciation, disinhibition, and interactivity. Moreover, online gamblers are usually able to select
from a greater variety of games and play multiple games in parallel on the internet which has
shown to be a risk factor for problematic gambling (Braverman et al., 2013; McCormack
et al., 2014). In a review of the available literature Gainsbury (2015) concluded that online
gambling did not cause gambling problems in, and of, itself. However, the review showed
that online gambling was more common among highly involved gamblers, and for some
online gamblers, this medium appeared to signicantly contribute to gambling problems.
Chóliz et al. (2021) analyzed the prevalence of gambling disorder in Spain, as well as dif-
ferences between online gambling (which was legalized in 2012) and traditional gambling,
according to gender and age group. Chóliz et al. (2021) had access to the authorized data-
bases of surveys carried out by the General Directorate of Gambling Regulation (Dirección
General de Ordenación del Juego, 2016). They found that there were dierences between
age groups with respect to gambling involvement but not with respect to the prevalence
of pathological gambling. A total of 12.5% of people younger than 26 years had gambled
online compared to 56% who had participated in any type of gambling. Females had a
signicantly lower prevalence of pathological gambling than males among all age groups,
indicating that gender is a particularly relevant variable in the prevalence of gambling disor-
der. The prevalence of pathological gambling among gamblers who had gambled online was
7.26%, whereas it was 0.69% among those who had not. Pathological gambling occurred
among gamblers who also gambled online at a frequency 10 times higher than in gamblers
who had not gambled online. Similar ndings were also reported by Griths et al. (2009)
using data from a nationally representative British sample (5% problem gambling preva-
lence rate among those who gambled online compared to 0.5% who did not). However, it
must be noted that almost all online gamblers also gamble oine and that these gamblers
should be described as ‘multi-modal’ gamblers rather than online gamblers. In fact, one
study using a nationally representative sample of British gamblers (i.e., Wardle et al., 2011)
reported no cases of problem gambling among those participants who only gambled online
and that the highest prevalence of problem gambling was among multi-modal gamblers
(4.3%) followed by those who gambled oine only (0.9%).
Researchers have also found that adolescents are vulnerable to developing online gam-
bling problems (e.g., Gainsbury 2015; Hubert & Griths, 2018). Part of the explanation
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Journal of Gambling Studies
involves the developmental characteristics of adolescence, which is a period of particu-
lar vulnerability to engage in multiple forms of risky behavior (Jessor, 1991) and develop
addiction problems due to immature self-regulation capacity, impulsivity, external locus of
control, and susceptibility to contextual factors (Hollén et al., 2020). Recent studies have
also demonstrated a signicant increase in online gambling behavior among females, as
well as changing trends in online gambling problem development (Hollén et al., 2020;
McCormack et al., 2014; Volberg et al., 2018).
Responsible Gambling
Only 5–10% of individuals who develop gambling problems seek treatment (Slutske, 2006).
Preventing problem gambling is as important as it is to provide treatment. Besides restric-
tions in gambling availability, promotion of responsible gambling is seen as a strategy for
preventing gambling problems (Williams et al., 2012). In previous years, a number of stud-
ies have been published with regard to the prevention of problem gambling among online
gamblers (e.g., Edgerton et al., 2016; Haefeli et al., 2011; Shi et al., 2021).
Personalized feedback is one responsible gaming tool which has been subject to several
studies using real gamblers on actual gambling websites (e.g., Auer & Griths 2014, 2015a,
2016, 2018, 2020). Researchers have hypothesized that gamblers are not able to keep track
of their gambling, especially for games with a high event frequency. Auer and Griths
(2017) compared gambler’s actual behavioral tracking data with their self-report data over a
one-month period. A total of 1335 Norwegian gamblers answered survey questions relating
to their gambling expenditure that was then compared with their actual gambling expendi-
ture. They found that the estimated loss self-reported by gamblers was correlated with the
actual objective loss but that gamblers with higher losses tended to have much more di-
culty and were much less reliable in estimating their gambling expenditure. They concluded
that feedback concerning actual spending is an important responsible gaming strategy.
Wohl et al. (2017) asked 607 Canadian gamblers who had enrolled in a casino-loyalty
program how much they had won or lost over a three-month period while using their loy-
alty card. Results indicated that gamblers who under-estimated their losses signicantly
reduced the amount they wagered as well as the amount they lost during a follow-up period.
In a sample of 1,015 online gamblers, Auer and Griths (2015a) found that gamblers who
received feedback about money and time spent signicantly reduced subsequent gambling
expenses. A follow-up study with a population of Swedish gamblers supported the ndings
(Auer & Griths, 2020). Two studies with real-world online gamblers have also shown that
normative feedback about other gamblers’ expenditure also appears to reduce subsequent
gambling behavior (Auer & Griths, 2015b, 2016).
In recent years the eectiveness of voluntary vs. mandatory responsible gaming tools
has increasingly been discussed. Ivanova et al. (2019) evaluated the impact of deposit limit
prompts on the frequency of voluntary deposit limit setting among a sample of Swedish
online gamblers. They reported that prompting online gamblers to set a voluntary deposit
limit of optional size did not aect subsequent net loss compared to unprompted customers.
Consequently, they concluded that voluntary limits are not an adequate responsible gam-
ing tool because only a small percentage of players use them. Delfabbro and King (2021)
conducted a review of mandatory vs. voluntary limit-setting research. They concluded gen-
eral support for the potential benets of mandatory systems. They also highlighted some
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Journal of Gambling Studies
potential selective uses for voluntary systems while also noting potential risks associated
with implementing mandatory global limits. In a commentary on Delfabbro and King’s
(2021) review, Auer and Griths (2021) noted that only ten of the 25 listed studies by Del-
fabbro and King were published in peer-reviewed journals and given the high reliance on
studies in the grey literature, there were other studies that could have been included. Auer
and Griths (2021) also listed other studies meeting Delfabbro and King’s inclusion crite-
rion that could have provided further useful data.
Peter et al., (2019) reviewed 11 studies with respect to the eect of personalized feedback.
They concluded that interventions appeared to be most eective when (i) used among popu-
lations of greater gambling severity, (ii) individuals were provided with gambling-related
educational information, and (iii) used in conjunction with motivational interviewing. Fac-
tors associated with reduced ecacy included in-person delivery of feedback without moti-
vational-interviewing and informing participants of their score on a psychological measure
of gambling severity. In a population of college-students, Larimer et al. (2012) found that
personalized feedback intervention (PFI) and cognitive–behavioral intervention (CBI) led
to reductions in gambling among at-risk or probable pathological problem gamblers. They
concluded that a single-session personalized feedback intervention and a multi-session cog-
nitive–behavioral intervention may be helpful in reducing disordered gambling among US
college students.
Neighbors et al. (2015) evaluated the impact of personalized normative feedback (PNF)
in a sample of 252 U.S. college students. Personalized normative feedback (PNF) is a brief
intervention designed to correct misperceptions regarding the prevalence of problematic
behavior by showing individuals engaging in such behaviors that their own behavior is
atypical with respect to actual norms. Their results supported the use of PNF as a standalone
brief intervention for at-risk gambling students. They also concluded that the intervention
eects were moderated by self-identication with other student gamblers, suggesting that
PNF works better at reducing gambling for those who more strongly identied with other
student gamblers.
Several studies have found promising results with regard to brief telephone and work-
book interventions for individuals with gambling problems (e.g., Abbott et al., 2012, 2018;
Hodgins et al., 2011). The phone-calls in these studies used a motivational interviewing
approach which encouraged gamblers to think about their gambling. In a review of the
existing literature, Yakovenko et al. (2015) concluded that motivational interviewing was
associated with signicant reduction in gambling frequency up to a year after treatment
delivery. They also found that for gambling expenditure, motivational interviewing yielded
signicant reductions in the amount of money spent gambling compared to players who
were not treated with motivational interviewing but this was at post-treatment only.
Only one previous study investigated the impact of personalized behavioral feedback by
telephone and letters on actual gambling expenditure in a sample of real-world gamblers
(i.e., Jonsson et al., 2019). In this study, a sample of 1,003 matched triplets was selected
from the top 0.5% of gamblers based upon their annual expenditure. Gamblers were ran-
domly assigned to the feedback intervention by telephone, letter, or a no-contact control
condition. The study found that over 12 weeks, theoretical loss1 decreased 29% for the
phone-call group and 15% for the letter group, compared to 3% of the control group. A
1 Theoretical loss is computed as amount of money wagered multiplied by the house advantage for each
game which was played (Auer et al., 2012).
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Journal of Gambling Studies
positive eect of the follow-up contact was limited to participants who at the initial call
indicated an interest in receiving a follow-up call. Jonsson et al. (2019) concluded that con-
tacting high-spending players about their gambling expenditure appeared to be an eective
method for gambling companies to meet their duty to care for their customers.
Gambling in the Netherlands
In 2021, the Netherlands (where the present study was carried out) introduced a new gam-
bling law which also includes licensed online gambling. Since October 2021, gambling
operators have been legally allowed to oer casino games as well as sports betting online for
Dutch residents. The regulation includes a number of player protection policies. Amongst
others, license holders have to monitor gambler behavior for indications of problematic
gambling. They are also obliged to interact with gamblers in case of a positive identication
of problem gambling. In a review of problem gambling worldwide Calado and Griths
(2016) cited two Dutch prevalence studies. The rst one comprised 5,575 participants
aged 16 years and over (Bruin et al., 2006). The ndings showed that 1% were probable
pathological gamblers (SOGS [South Oaks Gambling Screen] 5+) and 1.5% were potential
problem gamblers (score of 3–4 in SOGS) – both lifetime prevalence rates. The past-year
prevalence rates for pathological and problem gambling were 0.3% and 0.6%, respectively.
The highest prevalence of problem gambling was present among males, among individuals
aged between 30 and 50 years and between 18 and 30 years, among ethnic minorities, and
among the unemployed.
The second prevalence survey was conducted in 2011 by Bieleman et al. (2011) com-
prising approximately 6,000 participants. The percentage of problem gambling (5 + on the
SOGS) was 0.15% and the prevalence of at-risk gambling (3–4 SOGS) was 0.68%. More-
over, the prevalence of recreational gamblers (< 3 in SOGS) was 64.4%. The rates of at-
risk and problem gambling did not change statistically between 2005 and 2011 (see also
Goudriaan 2014). The authors are not aware of any Dutch prevalence studies in the past
decade or since the introduction of online gambling in October 2021. Ipsos Research (2022)
conducted a study after the introduction of online gambling in the Netherlands and found
that the number of Dutch people who had played online in the past 12 months remained the
same compared to the previous year (i.e., approximately 11% of the adult population). Of
the Dutch who played online since 1 October 2021, 78% had only done so with providers
licensed in the Netherlands.
The Present Study
Personalized feedback interventions (PFIs) are becoming a common practice among online
gambling operators (Harris & Griths, 2017). Furthermore, most European regulations
require online gambling operators to interact with gamblers in case of high expenditure or
indications of problematic gambling. However, there is limited research about the eects
of PFIs. The present study aimed to provide insight on the impact of PFIs on subsequent
gambling behavior among a Dutch sample of real-world gamblers.
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Journal of Gambling Studies
Method
Participants
Nederlandse Loterij (the national Dutch Lottery operator which oers sports-betting and
casino on the website toto.nl) provided access to a secondary dataset comprising tracking
data from online casino and sports betting gamblers (N = 2,576) who were contacted either
by e-mail or telephone between November 2021 and March 2022 if they showed signs of
problematic gambling as identied using behavioral tracking software. Players who were
contacted multiple times by e-mail, telephone or both were excluded from the analysis in
order to be able to assign the eect to one type of contact. Of the 2,576 gamblers, 1,876 were
contacted by e-mail and 700 were contacted by telephone. The average age of participants
was 41.73 years (SD = 13.34) and 34% of the sample were females (N = 874). Nederlandse
Loterij utilizes Mentor, a commonly used behavioral tracking tool used for the identication
of problematic gambling (Auer & Griths, 2020). Mentor’s risk classication was part of
the process which led to the selection the contacted gamblers. The present authors evaluated
the extent to which the contacts by email or telephone had an eect on their subsequent
gambling behavior.
Rationale for Matched Pairs Design
The main aim of the present study was to determine whether the receiving of an e-mail or
a telephone call by the gambling operator had an eect on subsequent playing behavior
compared to those gamblers who were not contacted. However, it is not appropriate to
simply compute the behavioral change after the contact. After the dataset was provided, the
present authors gave very careful consideration to all of the ways in which the data could
be analyzed. Following an initial inspection of the data, it became clear that analyzing the
behavioral change before and after the gamblers who were contacted (i.e., within-group
analysis) would not be particularly meaningful because there was very large variation in the
amount of time and money that the individuals’ spent gambling. For instance, some gam-
blers spent a lot of money gambling every day before being contacted while others spent
comparably little. The resulting mean average dierences in terms of money spent gambling
would likely be spurious because of the large individual dierences in gambling behavior.
Furthermore, there was no way of assessing whether the dierence in the amount of money
spent gambling within group was statistically signicant because there was no reliable com-
parison point. Therefore, a control group was needed.
One way to determine a valid control group is via a matched pairs design in which similar
gamblers out of the population are assigned to each of the 2,576 target group members who
were contacted between November 2021 and March 2022. Similarity with respect to wager-
ing and depositing was computed based on the 30 days prior the contact. In the remainder
of the present paper, the 30 days prior to contact being made with the gambler is referred to
as time period 1 (T1), and the 30 days after contact was made with the gambler is referred
to time period 2 (T2). The control group population only comprised gamblers that were
not contacted but who were most similar to the target group with respect to their behavior
between November 2021 and March 2022. Matched pairs for the target group members
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Journal of Gambling Studies
were chosen using the following criteria and was very similar to the procedures employed
in previous studies (e.g., Auer & Griths 2015a; Auer et al., 2018):
●Age: Control group members had to be at most ve years younger or older as the target
group member.
●Gender: Control group members had to be the same gender as the target group member
for matching purposes.
●Amount wagered 30 days prior to contact: Control group members had to have the same
amount wagered as the target group. For instance, if a target group member’s amount
wagered was €1,000, the control group member’s amount wagered needed to be within
€900 to €1,100 in order to be considered for matching purposes.
●Amount deposited 30 days prior to the contact: Control group members had to have the
same amount deposited as the target group. For instance, if a target group member’s
amount deposited was €1,000, the control group member’s amount deposited needed to
be within €900 to €1,100 in order to be considered for matching purposes.
This matching procedure ensured that a target group member was assigned one or more con-
trol group members only if the monetary gambling intensity and demographic prole was
most similar. All of the four criteria in the present study (i.e., age, gender, amount wagered,
amount bet) were weighted equally. For that reason, each target group member was matched
with none, or one or more control group members (as described above). Out of the 2576
target group gamblers who had received at least one feedback message during November
2021 and March 2022, 1,592 gamblers (62%) were assigned at least one control group
member who was not contacted. Therefore, 38% of the target group members did not match
any control group member with respect to the four criteria. This is similar to that reported by
Auer et al. (2020) who also utilized a matched-pairs design and reported that 40% of target
group gamblers could not be matched to any control group member. Unmatched gamblers
were subsequently discarded from the analysis.
If a target group member was matched with several control group members, the average
amount wagered and amount deposited in the 30 days before the contact date was com-
puted for all the matched control group gamblers for this specic target group member. The
matched gamblers were therefore aggregated to one “virtual” control group gambler for
each target group gambler. In order to determine the eect for each gambler, the amount of
money wagered, amount of money deposited, number of monetary deposits, amount of time
spent gambling, and gambling frequency (i.e., number of gambling days) in T2 was divided
by the same metrics in T1. This indicator was the ‘ratio’.
Consequently, the smaller the ratio, the lower the subsequent gambling intensity (in
terms of the amount of money wagered, amount of money deposited, number of monetary
deposits, amount of time spent gambling, and gambling frequency), and therefore the higher
the eect of the contact made with the gambler. Each target group member’s computed ratio
was compared to the ratio of the respective virtual matched pairs gambler for each of the
ve metrics relating to the time and money spent gambling. If a target group member’s ratio
was smaller than the respective control group’s ratio it was concluded that the target group
member’s behavior decreased more as a consequence of the contact compared to the con-
trol group members who were not contacted. Therefore, for each target/control pair, binary
variables were computed with respect to each of the ve metrics. The actual dierence was
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Journal of Gambling Studies
not analyzed because the dierent target/control pairs showed large individual variation.
The way the study was designed was to ensure that the gambling behavior between the two
groups were comparable (and is why the matched pairs design was chosen).
Statistical Analysis
The authors tested whether the ve metrics relating to the time and money spent gambling
followed a normal distribution according to D’Agostino (1971). Nonparametric Kruskal
Wallis tests were used for group comparisons (Kruskal, 1952). The authors used the pro-
gramming language Python (Van Rossum, 2007) to analyze the dataset. The amount of
money deposited (K = 3033, p < 0.001), amount of money wagered (K = 2140, p < 0.001),
number of monetary deposits (K = 2603, p < 0.001), time spent gambling (K = 383, p < 0.001),
and number of gambling days (K = 2164, p < 0.001) in the 30 days prior to being contacted
signicantly deviated from a normal distribution. The eect of e-mail or phone contacts
made by the gambling operator was analyzed with respect to the ve metrics relating to
time and money spent gambling. Gambling frequency was measured using the number of
distinct days on which at least one wager was placed. A signicance level of 1% was used
for statistical testing.
Results
Matched-pairs Analysis
A total of 2,576 gamblers were either contacted by e-mail or called by the gambling operator
between November 2021 and March 2022. Out of the 2,576 gamblers, 1,592 were matched
with at least one gambler from the control group. Table 1 reports age, gender, amount of
money deposited, and amount of money wagered 30 days prior to being contacted for gam-
blers who were matched with any control group member and those who were not. In the
group of gamblers who received an e-mail the ones who were matched were younger and
less frequently female. The corresponding Mann-Whitney U-Test regarding the age dif-
ference (U = 636,321, p < 0.001) and the z-test regarding the gender dierence (z=-12.89,
p < 0.001) were both signicant.
Gamblers who were matched (compared to those who were not matched) deposited less
money and wagered less money in the 30 days prior to the e-mail. The corresponding Krus-
kal-Wallis tests were signicant (K = 634236.5, p < 0.001; K = 645,387, p < 0.001). The same
pattern was observed for the group of gamblers who were not matched. The corresponding
statistical tests for age (U = 58,381, p < 0.001), gender (Z=-5.34, p < 0.001), amount of money
deposited (U = 42,313, p < 0.001), and wagered (U = 42,755, p < 0.001) were all signicant.
Table 1 also shows higher monetary deposits and wagers for gamblers who were called
by telephone compared to gamblers who received an e-mail. This was the case for both
matched and unmatched gamblers. Furthermore, the mean values for the amount of money
wagered and deposited were larger than the corresponding median values. Gamblers who
were called by telephone and matched with at least one control group member deposited
on average €15,938 in the 30 days prior to the call. The corresponding median value was
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Journal of Gambling Studies
€12,516. This large dierence between mean and median means that a small number of
gamblers generated a large amount of money deposited and wagered, respectively.
On average, each target group member was matched with eight control group gamblers.
The minimum number of matches was one and the maximum was 158. The size of the con-
trol group was 369,961 gamblers. The assignment of multiple controls to one target group
member was based on the recommendations of Miettinen (1969). More recently, Ming and
Rosenbaum (2000) noted that matching with a xed number of controls may remove only
50% of the bias in a covariate, whereas matching a variable with many controls may remove
up to 90% of the bias.
Global Effect of e-mail vs. Telephone Contact by the Gambling Operator
The eect that personalized contacts had on subsequent monetary depositing, monetary
wagering, deposit frequency, gambling time duration, and gambling frequency of those who
were contacted by the gambling operator was statistically analyzed and compared with that
of the control group. It was assumed that any dierence between the gambling behavior in
the two groups could be due to chance and would be similar to the tossing of a coin. For that
reason, it was assumed under the null hypothesis that in 50% of the cases the target group’s
gambling behavior (as measured by the amount of time and money spent) would be higher
than the control group’s gambling behavior and in 50% of the cases the control group’s gam-
bling behavior (as measured by the amount of time and money spent) would be higher than
the target group’s gambling behavior. Therefore, it was assumed that any deviation from the
distribution would be due to the eect of being contacted by the gambling operator. Conse-
quently, the dierence between the actual observed percentage to the expected percentage
(50%) of gambling behavior was statistically examined.
Of the 1,208 matched target group members who were contacted via e-mail (and com-
pared to the ratio of the matched control group members), 750 showed a smaller amount of
money deposited ratio (62%), 727 showed a smaller amount of money wagered ratio (60%),
671 showed a smaller number of monetary deposits ratio (56%), 655 showed a smaller time
spent gambling ratio (54%), and 596 showed a smaller gambling frequency ratio (49%).
Except for gambling frequency, gambling behavior decreased more among the group of
gamblers who were contacted by email by the gambling operator compared to the matched
control group members. The resulting ratios reported above were compared to the expected
ratio of 50% using a z-test. The results showed signicant dierences for amount of money
deposited (z = 8.66; p < 0.001), amount of money wagered (z = 7.23, p < 0.001), number of
monetary deposits (z = 3.88, p < 0.001) and time spent gambling (z = 2.95, p = 0.0032). The
Table 1 Demographic distribution and spending 30 days prior to e-mail/telephone contact for gamblers who
were matched and gamblers who were not be matched with any control group member
Age (years) Female Amount deposited
(€)
Amount wagered
(€)
Matched Contact N Mean SD N % Mean Median Mean Median
No E-mail 668 42.56 12.61 355 53% 7497 5296 63,881 40,566
Yes E-mail 1208 39.31 10.45 313 26% 5036 4081 37,832 29,988
No Telephone 316 40.56 12.15 125 40% 15,938 12,516 151,159 91,609
Yes Telephone 384 37.26 9.49 81 21% 8265 7150 60,736 48,345
2576 40.11 11.27 874 34% 5912 4431 47,107 32,543
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Journal of Gambling Studies
49% ratio reported for playing frequency did not signicantly deviate from the expected
ratio of 50% (z=-0.46, p = 0.65). Therefore, the e-mail contact had the desired impact on
subsequent playing behavior with respect to monetary spend and time spend, but not gam-
bling frequency.
Of the 384 matched target group members who were contacted by telephone (and com-
pared to the ratio of the matched control group members), 252 showed a smaller amount of
money deposited ratio (66%), 240 showed a smaller amount of money wagered ratio (62%),
241 showed a smaller number of monetary deposits ratio (63%), 226 showed a smaller
gambling duration ratio (59%), and 213 showed a smaller gambling frequency ratio (55%).
Gambling behavior decreased more among the group of gamblers who were contacted by
telephone by the operator compared to the matched control group members with respect to all
ve behavioral metrics. The resulting ratios reported above were compared to the expected
ratio of 50% using a z-test. The results showed signicant dierences for amount of money
deposited (z = 6.45; p < 0.001), amount of money wagered (z = 5.06, p < 0.001), number of
monetary deposits (z = 5.17, p < 0.001), gambling time duration (z = 3.53, p = 0.0004) and
gambling frequency (z = 2.15, p = 0.03). The 55% ratio reported for gambling frequency did
not signicantly deviate from the expected ratio of 50% (z=-0.46, p = 0.65). Therefore, the
telephone contact had the desired impact on subsequent playing behavior with respect to
monetary spend and time spend, but not gambling frequency.
Effect by Gambling Operator Contact type
Table 1 shows that gamblers who were contacted by telephone wagered and deposited more
money than gamblers who were contacted by e-mail. Table 2 shows the eects of the ve
metrics with respect to the control group by contact type. The average eects were consis-
tently larger in the group of gamblers who were contacted by telephone. However, none of
the dierences were statistically signicant.
Effect of Gambling Operator Contact by Gambling Intensity
Analysis was also carried out to see if gambling intensity was associated with the eect of
the contact by the gambling operator. To do this, the target group members were divided into
four equally sized groups according to their amount of money wagered in the 30 days before
the contact. This was done separately for the gamblers who received an e-mail and gamblers
who received a telephone call. Table 3 shows the eects in the e-mail group with respect to
the ve metrics in each of four intensity groups. Chi-square tests showed that none of the
ve metrics’ eects were signicantly dierent between the four intensity groups. Krus-
kal-Wallis tests also showed that the four intensity groups did not dier with respect to
E-mail Telephone
N 1208 384 1592
Amount deposited ratio 62% 66% z=-1.25, p = 0.21
Amount wagered ratio 60% 62% z=-0.81, p = 0.41
Number of deposits ratio 56% 63% z=-2.49, p = 0.013
Gambling duration ratio 54% 59% z=-1.59, p = 0.11
Gambling frequency ratio 49% 55% z=-2.10, p = 0.036
Table 2 Eects by contact type
(e-mail vs. telephone)
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Journal of Gambling Studies
age. However, there was a signicant dierence with respect to gender. The percentage of
females (14%) was lowest among the 25% of gamblers who wagered the most money in the
30 days prior to the e-mail contact. The percentage of females did not vary meaningfully
in the other three groups. The overall average percentage of females among gamblers who
received e-mail contact from the gambling operator and were matched was 26%.
Table 4 shows the eects of receiving a telephone call from the gambling operator with
respect to the ve metrics in each of the four intensity groups. The four groups were based
on the amount of money wagered 30 days prior to receiving a telephone call. Chi-square
tests showed that none of the ve metrics’ eects were signicantly dierent between the
four intensity groups. Kruskal-Wallis tests also showed that the four intensity groups were
not signicantly dierent with respect to age. However, there was a signicant dierence
with respect to gender. The percentage of females (7%) was lowest in the 25% of gamblers
who wagered the most money in the 30 days prior to receiving an e-mail. The percentage of
females was largest (34%) in the 25% of gamblers who wagered the least amount of money.
The overall average percentage of females among gamblers who received e-mail contact
from the gambling operator and were matched was 21%.
Table 3 Eects of e-mail contact in four intensity groups based on amount wagered 30 days prior to contact
by the gambling operator
Quantile amount wagered 1 2 3 4
N 302 302 302 302 1208
Amount deposited ratio 64% 62% 62% 61% χ2 = 0.59, p = 0.90
Amount wagered ratio 59% 59% 62% 61% χ2 = 0.98, p = 0.81
Number of deposits ratio 54% 58% 57% 53% χ2 = 0.59, p = 0.90
Gambling duration ratio 51% 55% 56% 54% χ2 = 1.61, p = 0.66
Gambling frequency ratio 44% 51% 50% 53% χ2 = 5.48, p = 0.14
Age 39.88
(SD = 11.02)
39.81
(SD = 10.91)
39.52
(SD = 10.50)
38.03
(SD = 9.18)
K = 3.64, p = 0.30
Female 94 (31%) 91 (30%) 84 (29%) 41 (14%) χ2 = 32.34,
p < 0.001
Table 4 Eects of telephone contact by the gambling operator in four intensity groups based on amount of
money wagered 30 days prior to contact
Quantile amount wagered 1 2 3 4
N 96 96 96 96 384
Amount deposited ratio 66% 67% 71% 59% χ2 = 2.86, p = 0.41
Amount wagered ratio 62% 60% 62% 65% χ2 = 0.35, p = 0.95
Number of deposits ratio 67% 64% 67% 54% χ2 = 1.90, p = 0.60
Gambling duration ratio 60% 56% 59% 59% χ2 = 0.39, p = 0.94
Gambling frequency ratio 56% 52% 55% 58% χ2 = 0.79, p = 0.85
Age 37.49
(SD = 11.97)
34.14
(SD = 8.74)
36.59
(SD = 8.00)
37.80
(SD = 8.88)
K = 0.9, p = 0.82
Female 33(34%) 21 (22%) 20 (21%) 7 (7%) χ2 = 21.20,
p < 0.001
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Journal of Gambling Studies
Effect of Gambling Operator Contact by Gender
Analysis was also carried out to see if gender was associated with the eect of the contact
by the gambling operator. Table 5 shows the eects of the contact by e-mail for females and
males. Except for gambling frequency, the eects were larger among males. However, none
of the respective z-tests were signicant.
Table 6 shows the eects of receiving a telephone call from the gambling operator for
females and males. The eects were larger among males compared to females. However,
none of the respective z-tests were signicant.
Effect of Gambling Operator Contact by age
In order to determine whether the eect of gambling operator contact varied across age, the
authors classied gamblers into ve age groups as shown in Tables 7 and 8. Table 7 reports
the eects of receiving e-mail contact by the gambling operator for each age group. None of
the ve metrics was signicantly dierent between the age groups. There was also no clear
pattern with respect to the size of the eects in the dierent age groups. Eects were neither
descending or ascending in the same way across age groups.
Table 7 Eects of gambling operator e-mail contact by age group
Age group (in years) <=33 34–43 44–53 54–63 >=64
N 428 399 242 115 24 1208
Amount deposited ratio 64% 64% 57% 61% 58% χ2 = 4.56, p = 0.33
Amount wagered ratio 64% 62% 56% 52% 54% χ2 = 8.07, p = 0.09
Number of deposits ratio 57% 56% 51% 61% 50% χ2 = 3.7, p = 0.45
Gambling duration ratio 56% 55% 52% 50% 46% χ2 = 2.52, p = 0647
Gambling frequency ratio 49% 50% 47% 54% 42% χ2 = 2.39, p = 0.66
Female Male
N 81 303 384
Amount deposited ratio 58% 68% z=-1.62, p = 0.10
Amount wagered ratio 60% 63% z=-0.42, p = 0.68
Number of deposits ratio 59% 64% z=-0.73, p = 0.17
Gambling duration ratio 52% 61% z=-1.44, p = 0.15
Gambling frequency ratio 54% 56% z=-0.23, p = 0.82
Table 6 Eects of gambling
operator telephone contact by
gender
Female Male
N 313 895 1208
Amount deposited ratio 57% 64% z=-2.35, p = 0.019
Amount wagered ratio 54% 62% z=-2.47, p = 0.014
Number of deposits ratio 54% 56% z=-0.77, p = 0.43
Playing duration ratio 52% 55% z=-0.88, p = 0.38
Playing frequency ratio 53% 48% z = 1.39, p = 0.17
Table 5 Eects of gambling op-
erator e-mail contact by gender
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Journal of Gambling Studies
Table 8 shows the respective numbers for gamblers which were contacted by telephone
by the gambling operator. None of the eects were signicant between the age groups.
There was also no clear pattern with respect to the distribution across age groups. Only one
gambler was at least 64 years old among the gamblers who were contacted by telephone.
Discussion
The present study investigated the eects of online casino and sports-betting gamblers being
contacted by Nederlandse Loterij either via e-mail or telephone call. The reason for the
contact was showing signs of problematic gambling which were identied via the player
tracking tool Mentor. Gamblers were not randomly assigned to one of the two groups. Con-
sequently, the authors chose a matched-pairs design to create a comparable control group.
Matched-pairs designs are commonly used to study causal eects in retrospective studies
and in situations where a randomized experimental set-up is not possible (e.g., Cummings et
al., 2002; Freedman et al., 1997). During the process, the 2576 target group gamblers were
matched with similar gamblers based on the amount of money wagered, amount of money
deposited, age, and gender. The control group gamblers were not contacted. Of the 2,576
gamblers who were contacted by the gambling operator, only 1,592 could be matched with
one control group (62%). This percentage is similar to the one reported by Auer et al. (2020)
in their study of a loss-limit reminder among Norwegian online gamblers. They also applied
a matched-pairs design because of the lack of randomized assignment. Similar to Auer et al.
(2020), gamblers who were not matched wagered more money and deposited more money
in the 30 days prior to being contacted by the gambling operator. This was the case for both
gamblers who received an e-mail or received a telephone call. Nederlandse Loterij’s main
reason for the contact were signs of problematic gambling and the authors assumed that the
majority of the most intense gamblers were therefore contacted. This explains why the non-
matched gamblers displayed higher gambling intensities.
Among the 1,208 matched gamblers who received an e-mail from the gambling oper-
ator, a signicant reduction in amount of money deposited, amount of money wagered,
number of monetary deposits, and time spent gambling were observed in the 30 days after
being contacted. Gambling frequency as measured by the number of gambling days did not
change signicantly. The same results were observed among the 384 matched gamblers
who received a telephone call from the gambling operator. The ndings support the only
previous comparable study by Jakobsson et al. (2019). They applied a fully randomized
experimental design and also found that high-intensity gamblers who received a letter or
a telephone call from the gambling operator (i.e., Norsk Tipping) subsequently reduced
Table 8 Eects of gambling operator telephone contact by age group
Age group (in years) <=33 34–43 44–53 54–63 >=64
N 156 133 68 26 1 384
Amount deposited ratio 63% 65% 72% 65% 100% χ2 = 2.15, p = 0.71
Amount wagered ratio 62% 59% 72% 54% 100% χ2 = 4.63, p = 0.33
Number of deposits ratio 65% 58% 71% 54% 100% χ2 = 4.86, p = 0.30
Gambling duration ratio 60% 57% 65% 46% 100% χ2 = 3.58, p = 0.47
Gambling frequency ratio 57% 52% 60% 50% 100% χ2 = 2.60, p = 0.63
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Journal of Gambling Studies
their monetary gambling expenditure. Although the group that received a telephone call
displayed larger reductions in their gambling than those who received e-mails, the dier-
ences were not statistically signicant. This contradicts the ndings by Jakobsson et al.
(2019) who found that telephone calls were more ecient than letters (although letters are
not the same as emails even though they are both print interventions). The present authors
hypothesize that the lack of signicance between the e-mail and the telephone call group
could also be due to the small sample size.
The present study also tested whether the eects of being contacted by the gambling
operator varied across gambling intensity. Gamblers contacted by e-mail as well as those
contacted by telephone were divided into four groups based on the amount of money
wagered in the 30 days prior to being contacted. None of the eects were signicantly
dierent between the four intensity groups. In their real-world matched-pairs study, the
participants in Auer and Griths’ (2015a) study were provided with personalized feedback.
However, there was no correlation between gambling intensity and the eect of personal-
ized feedback in that study.
There was a signicant correlation between gender and gambling intensity. Females were
less likely to be among the most intense gamblers. This holds true for gamblers in the group
that received an e-mail from the gambling operator as well as in the group that received a
telephone call. A number of previous studies have found that males gamble more intensely
or having a higher likelihood of problem gambling than females (e.g., Ivanova et al., 2019;
Husky et al., 2015; Kairouz et al., 2016). With the exception of gambling frequency, the
eects were larger among males than among females. However, none of the dierences
were signicantly dierent. A larger sample size would most likely have led to signi-
cant results. The authors also wanted to test if there were any age dierences with respect
to the eects of the contact. However, no signicant dierences or obvious patterns were
observed.
Limitations
The present study is only the second attempt to investigate the eect of an online gambling
operator contacting gamblers displaying potentially problematic gamblers. Despite the
many strengths of this study, there are a number of limitations. Nederlandse Loterij selected
the high-risk gamblers who were contacted either by telephone or by e-mail. For that rea-
son, not all high-risk gamblers could be matched with a control group member because
the highest spending gamblers were all selected to be contacted. This creates a bias and
the conclusions do not apply to the highest spenders. This is simply a consequence of the
thoroughness of the operators safer gambling procedures and the regulatory requirements.
One of the major limitations of the present study was that data were only collected from
one gambling environment in one particular country (Netherlands). Replicating the results
with other online gambling operators’ websites from dierent countries would help further
corroborate the ndings reported here. Another limitation is that there is no way of know-
ing whether the target group gambled with other online operators during the experimental
period. Studies such as the British Gambling Prevalence Surveys (Wardle et al., 2007, 2011)
have shown that at-risk and problem gamblers in particular engage with numerous gambling
websites and gambling forms. Not being able to conrm such assertions through self-report
methodologies is arguably another limitation of the study. There is also the possibility that
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Journal of Gambling Studies
more than one person gambled using the same account (e.g., a husband and wife) although
the number of instances where this occurred is likely to be low. Future studies could exam-
ine the impact on gambling behavior by comparing the impact of one message reminder
compared to multiple reminders.
Conclusion
The present study found that online gamblers reduced both the money and time spent gam-
bling when contacted either by an e-mail or telephone call by the online gambling operator.
This is an important nding, given that regulators increasingly require operators to interact
with gamblers and evaluate the eects of such personalized interactions. The study also
showed that e-mails appear to be as eective as telephone calls. E-mails require less person-
nel than telephone calls which means that more gamblers can be informed and the infor-
mation could also be tailored to a gambler’s individual gambling patterns. The results also
showed that a reduction of gambling expenditure on both time and money was as likely
among high-spending gamblers as among low-spending gamblers.
The present study is the latest in a growing number of studies that have evaluated the
ecacy of responsible gambling tools in real world settings using real gamblers engaging
in real time gambling on real gambling websites (as opposed to ecacy evaluations in
laboratory situations where the sample sizes are often very small and not necessarily repre-
sentative of real gamblers because of the use of convenience sampling). The results of the
present study are of use to many dierent stakeholder groups including researchers in the
gambling studies eld (who can attempt to replicate and extend the present study in other
jurisdictions and cultures), and the gambling industry (who can employ such responsible
gambling tools knowing there is an empirical base demonstrating their ecacy), as well as
regulators and policymakers who can recommend or enforce that gambling operators utilize
responsible gambling tools such as contacting those who may be displaying problematic
gambling behaviors as a way of minimizing harm and protecting gamblers.
Supplementary Information The online version contains supplementary material available at https://doi.
org/10.1007/s10899-022-10162-2.
Author’s contribution Both authors contributed to the preparation of this manuscript.
Funding None received.
Data Availability The data for this study are not available due to commercial sensitivity.
Declarations
Conflict of Interest The second author’s university has received funding from Norsk Tipping (the gambling
operator owned by the Norwegian Government). 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 Gamble Aware (formerly the Responsibility in Gambling Trust), a charitable body
which funds its research program based on donations from the gambling industry. Both authors undertake
consultancy for various gambling companies in the area of social responsibility in gambling.
Ethical approval Ethical approval was provided by the ethics committee of Nottingham Trent University.
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Journal of Gambling Studies
Informed consent Not applicable. Secondary data analysis.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence,
and indicate if changes were made. The images or other third party material in this article are included in the
article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is
not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright
holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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Authors and Aliations
MichaelAuer1· Mark D.Griths2
Mark D. Griths
mark.griths@ntu.ac.uk
Michael Auer
m.auer@neccton.com
1 neccton Ltd, Muhlgasse 23, 9900 Lienz, Austria
2 International Gaming Research Unit, Psychology Department, Nottingham Trent University,
50 Shakespeare Street, NG1 4FQ Nottingham, UK
1 3
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