International Gambling Studies

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Skill gambling machines and electronic gaming
machines: participation, erroneous beliefs, and
understanding of outcomes
Sally M. Gainsbury , Kahlil S. Philander & Georgia Grattan
To cite this article: Sally M. Gainsbury , Kahlil S. Philander & Georgia Grattan (2020)
Skill gambling machines and electronic gaming machines: participation, erroneous beliefs,
and understanding of outcomes, International Gambling Studies, 20:3, 500-514, DOI:
10.1080/14459795.2020.1828991
To link to this article: https://doi.org/10.1080/14459795.2020.1828991

Published online: 19 Oct 2020.

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INTERNATIONAL GAMBLING STUDIES
2020, VOL. 20, NO. 3, 500–514
https://doi.org/10.1080/14459795.2020.1828991

Skill gambling machines and electronic gaming machines:
participation, erroneous beliefs, and understanding of
outcomes
Sally M. Gainsbury

a

, Kahlil S. Philander

a,b

and Georgia Grattana

a

Science Faculty, Brain and Mind Centre, School of Psychology, University of Sydney, Sydney, Australia;
Carson College of Business, School of Hospitality Business, Washington State UniversityManagement,
Everett, WA, USA

b

ABSTRACT

ARTICLE HISTORY

Skill-based gaming machines (SGMs) include a skill-element within
the random mechanisms of electronic gaming machines (EGMs).
Concerns have been expressed that SGMs may increase erroneous
beliefs among gamblers, which would exacerbate gambling pro­
blems. This paper presents the results of a survey of 184 Mechanical
Turk workers with access to SGMs. Exploratory analyses were con­
ducted on measures assessing understanding of the role of skill vs.
chance in determining outcomes in SGMs, EGMs, and other gam­
bling and gaming activities, gambling participation, problem gam­
bling severity, and gambling-specific erroneous beliefs. SGM play
was greater among participants who were younger, more fre­
quently played mobile games or gambled on EGMs, and had higher
problem gambling severity. Participants with prior SGM play experi­
ence did not have a greater understanding of SGMs, and had less
accurate understanding of how EGMs operate, yet had a higher selfreported understanding. The results suggest that individuals with
existing gambling problems may gamble on SGMs and that SGMs
may also appeal to a new cohort who do not engage with existing
gambling activities. Greater efforts are needed to enhance under­
standing of EGMs in addition to SGMs where these are available to
enabled informed decision-making and reduce erroneous beliefs
that may drive problematic play.

Received 4 July 2019
Accepted 26 August 2020
KEYWORDS

Electronic gaming machines;
erroneous beliefs; game;
irrational beliefs; skill

Skill-based gaming machines (SGMs) are a novel gambling activity that combines
traditionally popular and profitable electronic gaming machine (EGM) mechanics with
skill and other gaming elements. Also referred to as skill-based gambling machines,
video/hybrid/interactive gaming machines/slots, these gambling activities are emerging
in various configurations. For example, some SGMs involve the addition of skill-based
bonus rounds to traditional reel-driven EGMs, while others include the addition of
chance-based prizes and underlying mechanics to machines that resemble video, mobile,
or arcade games.

CONTACT Sally M. Gainsbury
sally.gainsbury@sydney.edu.au
and Mind Centre, 94 Mallet St, Camperdown NSW 2006, Australia
© 2020 Informa UK Limited, trading as Taylor & Francis Group

Gambling Treatment and Research Clinic, Brain

INTERNATIONAL GAMBLING STUDIES

SGMs are still early in their product life cycle. Their limited legal availability suggests
that ongoing concerns are held by regulators, including the potential for the machines to
increase gambling problems and impact vulnerable people such as young adults and
individuals with gambling problems (Toscano, 2018). Recent review and commentary
papers provide a good overview of the current styles of SGMs and potential impact for
consumers (Delfabbro et al., 2020; Pickering et al., 2020). SGMs can incorporate skill in a
variety of ways, and the extent to which players can impact outcomes varies between
games; however, chance still determines the overall outcomes and all players have a
chance of winning regardless of skill, and will occur losses over time. Two central issues
for SGMs have received little academic study: 1) consumer understanding of SGM and
the extent to which skill determines outcomes; 2) SGMs’ potential impact on gamblingrelated harms.
By offering a new player experience that is distinct from traditional EGMs (slots,
pokies, fixed odd betting terminals, fruit machines, video lottery terminals, etc.),
SGMs present an opportunity for new customers and thus increased revenue. SGMs
are described as being positioned to be adopted by younger generations who play
many types of online games and create new streams of revenue for gambling venues
(Parker, 2016). This rationale is based on indications that EGMs are less popular
among millennials and Generation Z than previous generations (Suh et al., 2017).
However, millennials are more likely to appreciate games such as Blackjack, which
have an element of skill or strategy and some social elements (Un, 2013). Online
game play, with a much higher skill and social component than most gambling is
popular among younger generations. An estimated 67% of U.S. adults aged 18 to
29 years, and 75% of Australians aged 25–34 years, report having played video games
at least once in their life (Brand et al., 2017; U.S. Average Age of Video Gamers, 2018).
The structural characteristics of EGMs, and their potential to encourage erro­
neous beliefs such as illusions of control, superstitions, and chasing losses (or wins),
have been discussed (Delfabbro & Winefeld, 2000; Lund, 2011). Misconceptions
about gambling are associated with increased consumption and problematic gam­
bling behaviors (Ciccarelli et al., 2017), and erroneous beliefs are associated with
persistent gambling to an unaffordable level (Goodie & Fortune, 2013; Myrseth et
al., 2010; Toneatto et al., 1997; Xian et al., 2008). Miller and Currie (2008) demon­
strated that erroneous beliefs moderate the relationship between risky gambling
behaviors and gambling intensity. If the use of SGMs is related to increased levels
of erroneous beliefs about gambling and lower accuracy of understanding of the role
of skill and chance in determining outcomes, this may reduce the ability of con­
sumers to make informed choices about play, and increase the risks of problematic
gambling associated with these new (Blaszczynski et al., 2004; Blaszczynski &
Nower, 2002).
Exposure effects (LaPlante & Shaffer, 2007) may be observed in the expansion of
SGMs. When new gambling activities are introduced to a market, they are often
engaged by individuals with high levels of gambling involvement and potentially
existing problems. This is demonstrated by high rates of gambling problems among
recent new gambling forms including Internet gambling (Gainsbury, 2015), esports
betting (S. M. Gainsbury et al., 2017; Macey & Hamari, 2018), and Daily Fantasy
Sports (Martin & Nelson, 2014). When controlling for overall gambling

S. M. GAINSBURY ET AL.

involvement, participation in the new forms is generally not a predictor of gambling
problems, however, they may exacerbate existing problems (Baggio et al., 2017;
LaPlante et al., 2014; Philander & MacKay, 2014). Conversely, if SGMs do attract
a new cohort this creates the potential for problems among a population that would
not otherwise have been exposed to gambling, thus increasing overall gambling
harms. If young adults are attracted to SGMs, this may be problematic as this
group has a higher rate of gambling problems compared to other age cohorts
(Abbott et al., 2018; S. Gainsbury et al., 2014; Welte et al., 2015).
There is limited evidence regarding the use of SGMs, which are currently only
legally available in handful of North American jurisdictions. One SGM manufac­
turer, GameCo, reports that after two and a half years of operation, the average
customer is approximately 25 years younger than most individuals that play EGMs
(GameCo, 2019). They also report three to four times more play without a loyalty
card than EGMs, suggesting individuals who play SGMs do not regularly play
EGMs, and SGMs are thus creating incremental revenue (GameCo, 2019). The
current study builds on a pilot study that explored consumer understanding of
SGMs using an ecologically valid sample of casino customers (ref blinded).
Following engagement with an SGM facilitated by the machine manufacturer,
members of the US casino loyalty program were invited to complete a brief survey
(N = 43, aged 31–74 years, 56% female, 58% novice SGM players). Responses to
questions eliciting accuracy of understanding how SGMs outcomes are determined
indicated a lack of subjectively strong understanding immediately following play.
When asked to assess relative skill and chance, participants reported SGMs were
higher in perceived skill than EGMs, lower than poker, but equivalent to blackjack.
No differences in game understanding were observed between participants based on
their previous SGM experience.
Research has not previously examined rates of gambling problems, game under­
standing, and erroneous beliefs amongst individuals who play SGMs. This research
aimed to explore the extent to which individual’s understand how outcomes are
determined in SGMs and the characteristics of those likely to play these. The
outcomes of this research have implications for gambling policy, including how to
regulate SGMs based on individual’s subjective experiences, informational strategies
needed to ensure individuals make informed decisions about play, and harm mini­
mization strategies. Understanding which individuals are likely to engage with
SGMs is important for regulators, but also for industry and public health stake­
holders to understand the likely impact on gambling adoption, revenue, and poten­
tial harms for individuals and the community.
This research was largely exploratory given the absence of related literature in
the field. We predicted that in a community sample, those who had played SGMs
would be younger than non-players, gamble more frequently, and have higher
rates of gambling problems than those who had not played SGMs. We predicted
that most individuals would not have a strong understanding of the role of skill vs.
chance in determining SGMs outcomes. The research protocol was approved by
the first author’s University Human Research Ethics Committee (2017/890). The
protocol was not pre-registered, the data and materials will be made available
through [link].

INTERNATIONAL GAMBLING STUDIES

Methods
Participants
The sample was recruited using Amazon Mechanical Turk, an online marketplace for
sourcing tasks to workers. Participants reviewed a participant information sheet and
completed a consent form prior to commencing the survey. To increase the probability of
recruiting individuals who had previously played SGMs, participants had to meet the
following inclusion criteria: a) be at least 21 years of age, b) speak fluent English, c) be
North American, and d) live in or have visited a state where SGMs are legal (NV, NJ, CT,
or CA) in the past 12 months. Mechanical Turk participants are more demographically
diverse compared to convenience samples (Casler et al., 2013) and more representative of
the US population (Berinsky et al., 2012; Buhrmester et al., 2011). To increase the
likelihood of high-quality responses, participants were restricted to those with a
Mechanical Turk approval rating of at least 95%, consistent with practices adopted in
previous research (Goodman et al., 2013). Participants were paid USD2 for an estimated
30 minutes of their time, an amount consistent with similar studies and deemed to not
represent a coercion to participate.
A total of 232 participants agreed to take the survey, 47 responses were removed due to
failing one or more attention checks (e.g., asked to give a specific answer to indicate that
they were paying attention, these questions were noted in the participant information
sheet, Rouse, 2015), and one was removed due to completing the survey in an unrea­
sonably fast time. To inform our initial power calculations, we used data on differences
between online and non-online gamblers from the 2010 British Gambling Prevalence
Study dataset – these data reflect differences between users and non-users of another
emergent form of gambling. We examine the power to detect problem gambling severity
index score differences (described in the measures section). The measure has a mean
difference of 1.182 with an outcome standard deviation of 1.253 in the external data. We
assume a power level of .8, an α = 0.05, and apply a factor scale of two (sd = 2.506) to the
standard deviation to allow for potentially higher variance in our sample. We calculate a
required a sample size of 71 participants per group. After removing responses, there were
184 responses for analysis. Of these, 104 participants who report never playing SGMs in
the past year, and 80 reported having played at least once, who were categorized as SGM
players. Within this group, 23 reported typically playing SGMs at least once per week, 23
at least once per month, and 34 several times per year.

Measures
SGM game understanding
Participants were asked about their understanding of SGMs. They were instructed that
items about ‘winning’ or ‘losing’ referred to outcomes overall their bets and that ‘out­
comes’ refers to winning/losing money. Items were arranged on a five-point Likert scale
(1 = strongly disagree, 5 = strongly agree) and used the term skill-based gambling
machines. A summative score (SGMs understanding) from 4 to 20 was computed
using the following items:

S. M. GAINSBURY ET AL.

(1) A player of greater skill is more likely to win money on the skill-based gambling
machines over 1 h of play, compared to a player of lesser skill.
(2) Over the long term, all players will lose money on the skill-based gambling
machines.
(3) (Reverse scored) The outcomes of skill-based gambling machines are random no
matter what a player does.
(4) With practice, a player can improve their outcomes on skill-based gambling
machines over time.
Self-reported SGMs understanding was assessed using an additional question:
(1) I understand how a player’s skill impacts the outcomes of skill-based gambling
machines.
EGM game understanding
Participants were asked about their understanding of EGMs using similar items as with
SGMs. The items were scored differently as a function of the differences between EGMs
and SGMs. Items were arranged on a five-point Likert scale (1 = strongly disagree,
5 = strongly agree), and a summative score (EGM understanding) from 4 to 20 was
computed using the following items:
(1) (Reverse scored) A player of greater skill is more likely to win money on EGM
machines over 1 h of play, compared to a player of lesser skill.
(2) Over the long term, all players will lose money on EGM machines.
(3) The outcomes of EGM machines are random no matter what a player does.
(4) (Reverse scored) With practice a player can improve their outcomes on EGM
machines over time.
Self-reported EGM understanding was assessed using an additional question:
(1) I understand how a player’s skill impacts the outcomes of EGM machines.
Role of skill and chance
Participants were asked about the perceived skill and chance involved in various games.
Items were arranged on a five-point Likert scale (1 = all chance, 5 = all skill). A measure of
skill/chance understanding was computed using responses to items on five different
games (EGMs, SGMs, poker, video games, and chess). Scores were computed such that
‘1’ was added to participants’ total if they identified EGM as ‘all chance,’ chess as ‘all skill,’
and each of SGMs, poker, and video games as a point between ‘all chance’ and ‘all skill.’
Possible scores thus provided an indication of the accuracy of understanding the role of
skill and chance and range from 0 to 5.
Play frequency
Participants were asked about their frequency of play in several games of chance, skill,
and mixed skill and chance. Games included SGMs, EGM machines, blackjack, poker,
video games, mobile games, and chess. Participants were shown videos of SGMs and

INTERNATIONAL GAMBLING STUDIES

EGM machines, and images of the other games with descriptions, to ensure they under­
stood the differences in the games. Items were measured on a 6-point scale, including,
‘not at all in the past year,’ ‘several times per year,’ ‘once per month,’ ‘several days per
month,’ ‘at least once per week,’ and ‘daily.’
Problem gambling severity index (PGSI). The PGSI is a nine-item scale used for
measuring the severity of gambling problems in the general population (Ferris &
Wynne, 2001). Items are measured on a 4-point scale (0 = never, 1 = sometimes,
2 = often, 3 = almost always), which are summed. Scores vary from 0 to 27.
Gambler’s Belief Questionnaire
The GBQ is a 20-item self-administered scale used to assess gambling-related cognitive
distortions (Winfree et al., 2015). Items are measured on a 7-point Likert scale
(1 = strongly agree, 7 = strongly disagree), which are reverse coded and summed.
Higher scores indicate higher levels of cognitive distortions. There are two subscales:
1) An 8-item illusion of control construct (GBQ-IC); and 2) a 12-item luck/perseverance
construct (GBQ-LP).
Demographics
Participants responded to demographic items, including their age, gender, highest degree
(education), and household income band.
Summary statistics of variables used in are provided in Table 1.
Analysis plan
To explore the relationships of interest, we first estimated bivariate relationships between
those variables and prior SGM participation. We then estimated an ordered logit model
using past 12-month SGM play frequency as a dependent variable, and any statistically
significant variables from our bivariate tests as explanatory variables. To produce the final
model, non-significant variables are eliminated in backward stepwise fashion, using an
α = 0.05 criterion. We use a stepwise model as they can efficiently identify covariates from
a large group of variables, but we note they can also produce frequent Type I errors
(Mundry & Nunn, 2009).

Results
In Table 2, we report the results of our Welch-Satterthwaite’s t-tests of differences in
means between those with previous SGMs experience and those without SGM experi­
ence. In the demographic, psychographic, and behavioral variables, only education and
household income fail to show a statistically significant difference in means. Individuals
who had played SGMs were younger, more likely to be male, have higher gambling
problems and erroneous beliefs, and were more likely to be involved in all gambling and
game types measured, as compared to individuals who had not played SGMs. We observe
individuals who had played SGMs to have less accurate skill/chance understanding, lower
EGM understanding, higher self-reported EGM understanding but no difference in
SGMs understanding or in self-reported SGMs understanding.

Age
Female
Education
Household income
PGSI score
GBQ-IC
GBQ-LP
EGM frequency
SGM frequency
Poker frequency
Blackjack frequency
Video Game frequency
Mobile Game frequency
Chess frequency
Skill/chance understanding
SGM understanding
Self-reported SGM understanding
EGM understanding
Self-reported EGM understanding

Mean
34.016
0.321
3.609
2.913
4.293
31.734
39.370
2.435
2.043
2.120
2.071
4.152
4.304
2.261
3.739
15.272
3.995
16.038
2.913

Table 1. Summary demographics of study participants.
SD
9.290
0.468
0.929
1.566
6.340
11.590
19.081
1.333
1.511
1.350
1.403
1.656
1.658
1.695
1.001
2.218
0.926
3.435
1.423

Minimum

Maximum

S. M. GAINSBURY ET AL.

Age
Female
Education
Household income
PGSI score
GBQ-IC
GBQ-LP
EGM frequency
Poker frequency
Blackjack frequency
Video game frequency
Mobile game frequency
Chess frequency
Skill/chance understanding
SGMs understanding
Self-reported SGMs understanding
EGM understanding
Self-reported EGM understanding

Mean for participants with no past SGM experience
36.913
0.385
3.500
2.875
1.279
27.096
29.942
1.702
1.567
1.385
3.875
3.846
1.500
4.144
15.423
3.913
17.490
2.548

Mean for participants with past SGM experience
30.250
0.237
3.750
2.962
8.213
37.763
51.625
3.388
2.837
2.962
4.513
4.900
3.250
3.212
15.075
4.100
14.150
3.388

Table 2. Independent samples t-tests of mean differences in past 12-month SGMs players and non-players.
T-statistic
5.361
2.171
−1.842
−0.380
−7.876
−6.958
−8.913
−10.067
−6.789
−8.271
−2.697
−4.607
−7.427
6.829
1.049
−1.397
7.225
−4.267

P-value
0.000
0.031
0.067
0.705
0.000
0.000
0.000
0.000
0.000
0.000
0.008
0.000
0.000
0.000
0.296
0.164
0.000
0.000

INTERNATIONAL GAMBLING STUDIES

S. M. GAINSBURY ET AL.

We report the results of our stepwise logit regression model in Table 3. After the
backward elimination method, four statistically significant variables are noted to be
related to the frequency of SGMs play: age, EGM frequency, mobile game frequency,
and PGSI score. The Pseudo R2 = .428. Consistent with predicted relationships, the
coefficients suggest that higher SGM involvement is associated with lower age, higher
EGM, and mobile game involvement, and greater gambling problems.
To better understand the relationship between these variables and SGMs play fre­
quency, we plot the marginal effects of each variable on each of the SGMs frequency
categories in Figure 1. Most variables show a monotonic relationship, except for the
‘several times per year,’ ‘once per month,’ and ‘several days per week’ categories in
relation to EGM frequency. Those estimated effects rise with initial involvement in
EGMs, but then fall as SGMs continue to rise. The marginal effects plots also show that
some participation in SGMs is increasing mobile game frequency and PGSI score and
decreasing in age.

Discussion
Our predictions were supported and were consistent with the pilot study (ref blinded).
SGMs were more likely to be played by a younger audience and those who regularly play
EGMs and mobile games, suggesting that SGMs appeal to both existing regular EGM
players and a new cohort of players. This is consistent with research on other newly
introduced forms of gambling (S. M. Gainsbury et al., 2017; Macey & Hamari, 2018;
Martin & Nelson, 2014), suggesting that individuals already involved in gambling will
trial new gambling products where these are available. As predicted, SGMs use was
greater among those with higher problem gambling severity index scores, suggesting that
individuals with gambling problems are engaging with this new form of gambling.
Previous SGM play experience did not increase the accuracy of understanding how
outcomes are determined on these products or self-reported understanding. However,
consistent with the greater engagement of SGMs among existing gambling cohorts with
higher gambling problem scores, this cohort had a poorer understanding of EGMs and
greater erroneous gambling beliefs in general.
Our results support previous findings that individuals who preferred skill games or
both skill and chance games have higher levels of erroneous beliefs compared to
individuals with a preference for chance gambling games only (Myrseth et al., 2010;
Toneatto et al., 1997). Studies in cognitive biases indicate that the opportunity to make
choices, maintain active involvement, and accumulate experience at a game increases
perception of personal control and creates overconfidence in personal skills and subse­
quent expectations of winning (Langer, 1975, 1977; Langer & Roth, 1975). The nature of
SGMs enables individuals to actively participate in the gambling experience and our
results suggest that this is related to greater illusions of control in individuals, over­
confidence in understanding how outcomes are determined, and greater gambling
problem severity. However, our results are non-causal and it is possible that the greater
problem gambling severity may account for higher erroneous beliefs in those who prefer
SGMs.
The results from these studies need to be considered along with the limitations of the
research. Our samples were non-probabilistic and represent those with potentially

Coefficient
0.935**
5.918***
1.404*
1.115**

Standard error
(0.022)
(1.557)
(0.195)
(0.040

Exponentiated coefficients and standard errors shown; N = 184; * p < 0.05, ** p < 0.01, *** p < 0.001; cutoff 1: 41.380**; cutoff 2: 407.895***; cutoff 3: 1363.130***; cutoff 4: 9767.866***; cutoff 5:
216245.501***.

DV: SGMs frequency
Age
EGM machine frequency
Mobile game frequency
PGSI score

Table 3. Final outcome of stepwise logistic regression model.

INTERNATIONAL GAMBLING STUDIES

S. M. GAINSBURY ET AL.

Figure 1. Plots of the estimated marginal effects of EGM frequency of play, mobile game frequency of
play, age in years, and PGSI score. Lines describe the estimated probability of given SGM play
frequency for levels of variables shown on x-axes.

limited experience with SGMs, so our results are preliminary in the sense that use and
understanding of SGMs are likely to change over time as consumers adapt to the presence
of these new gambling activities. Further research is needed with more representative
populations, including those without previous gambling experience, and to assess the
range of entertainment preferences of those attracted to SGMs. The current study was
predominantly exploratory, so no aspects of causality are considered. Ongoing research,
including experimental, longitudinal, or repeated measures studies, and ecologically valid
field trials, is essential to examine the use of and understanding of SGMs over time. Early
adopters of SGMs may also be quite different than the consumer base once the technol­
ogy is more established (Mahajan et al., 1990).

Conclusions
This research suggests that SGMs are likely to appeal to both a new cohort of customers,
including those who engage with mobile games, and existing gambling customers. SGM
gambling does not appear to enhance understanding of how outcomes are determined
for these devices, which could limit the ability for informed decision-making, which is
held as a central tenant for responsible gambling (Blaszczynski et al., 2004). However,
given that lower understanding of EGMs is related to erroneous beliefs and problem
gambling severity, the findings indicate that efforts are needed not only to enhance
understanding of the role of skill vs. chance for SGMs, but for existing EGMs in an effort

INTERNATIONAL GAMBLING STUDIES

to reduce problematic gambling. The market for SGMs is evolving and it is likely that the
trajectory of this new type of gambling will be influenced by regulatory decisions and
consumer acceptance. This initial research is a first step in understanding who is likely to
engage with SGMs and their relationship with gambling involvement, irrational cogni­
tions, and gambling problems.

Disclosure statements
Funding
This work was supported by an Australian Research Council Discovery Early Career Research
Award [DE1060100459] awarded to Dr. Sally Gainsbury. ARC had no involvement in any aspects
of the research including, but not limited to, the research questions, methodology, research
conduct, or analysis of results.

Competing interests
Sally Gainsbury is the Editor of International Gambling Studies and were blinded from the review
and editorial process. Her competing interests are declared on the IGS website.
Since 2016, Kahlil Philander has received research funds from the Washington State Gaming
Commission, Manitoba Gambling Research Program, UNLV International Centre for Gaming
Regulation, U.S.-Japan Business Council, Board of Regents of the Nevada System of Higher
Education. He has received consulting payments from British Columbia Lottery Corporation,
Responsible Gambling Council of Canada, Intralot, the Commonwealth of The Bahamas, West
Virginia Lottery, Indiana Gaming Commission, and iDevelopment and Economic Association. He
has received reimbursement for travel from the National Conference of State Legislators, National
Council for Problem Gambling, International Association of Gaming Advisors, National Centre
for Responsible Gambling, Responsible Gambling Council of Canada, North American State and
Provincial Lottery Association, Evergreen Council on Problem Gambling, Global Gaming Expo
Asia, and Alberta Gambling Research Institute. He is a member of the National Council for
Problem Gambling, and formerly was the Director of Social Responsibility at the British Columbia
Lottery Corporation.
Georgia Grattan has no competing interests to declare.

Notes on contributors
Dr. Sally M. Gainsbury is Associate Professor in the School of Psychology and Co-Director of the
Gambling Treatment and Research Clinic within the University of Sydney. Her research focuses
on understanding the impact of new technology on gambling and behavioral addictions and
interventions to minimize harms.
Kahlil S. Philander is an Assistant Professor at Washington State University’s Carson College of
Business, and an Honorary Lecturer at the School of Psychology at the University of Sydney. His
research interests are in the socio-economic impacts of gambling.
Georgia Grattan was a student at the Gambling Treatment and Research Clinic at the University of
Sydney. She is currently completing the final year of a Master of Clinical Psychology at Queensland
University of Technology

Data availability statement
The data described in this article are openly available in the Open Science Framework at https://
doi.org/10.17605/OSF.IO/TPA6U.

S. M. GAINSBURY ET AL.

Open scholarship

This article has earned the Center for Open Science badges for Open Data and Open Materials
through Open Practices Disclosure. The data and materials are openly accessible at https://doi.org/
10.17605/OSF.IO/TPA6U.

ORCID
Sally M. Gainsbury
Kahlil S. Philander

http://orcid.org/0000-0002-9641-5838
http://orcid.org/0000-0002-0747-0772

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