new media & society
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What predicts esports
betting? A study on
consumption of video
games, esports, gambling
and demographic factors
Tampere University, Finland
University of Nevada, Las Vegas, USA; University of California, Los Angeles, USA
Tampere University, Finland; University of Turku, Finland
The parallel media related to sports, gaming and gambling are expanding, exemplified
by the emergence of esports and game-related gambling (e.g. loot boxes, esports
betting). The increasing convergence of these phenomena means it is essential to
understand how they interact. Given the expanding consumer base of esports, it is
important to know how individuals’ backgrounds and consumption of game media may
lead to esports betting. This study employs survey data (N = 1368) to investigate how
demographics, alongside consumption of video games, esports and gambling can predict
esports betting activity. Results reveal that both spectating esports and participation
in general forms of gambling are associated with increased esports betting, no direct
association was observed between the consumption of video games and esports betting.
Findings suggest that while games may act as a vehicle for gambling content, highlighting
the convergence of gaming and gambling, there is no intrinsic aspect which directly
encourages gambling behaviours.
Joseph Macey, Gamification Group, Faculty of Information Technology and Communication Sciences,
Tampere University, FIN-33014 Tampere, Finland.
908510NMS0010.1177/1461444820908510new media & societyMacey et al.
2 new media & society 00(0)
Betting, consumption, convergence, digital media, esports, gambling, gaming, MSSC,
The increased role of video gaming as a social and cultural force, combined with the
development of online multiplayer games and video streaming services, has resulted in
the growth of esports as a consumable media product. With its roots in the arcade culture
of the 1980s and LAN parties of the 1990s, esports is very much a phenomenon that has
emerged from the video gaming community (Borowy and Jin, 2013; Taylor and
Witkowski, 2010). Its rapid growth and wide appeal has seen it gather increasing atten-
tion from mainstream media and, due to the appeal it holds for millennial audiences,
businesses (Jenny et al., 2018; Newzoo, 2018).
Alongside the development and expansion of esports, a parallel trend can be observed:
gambling as related to video games, and to esports in particular. Indeed, the two seemed
to be inextricably linked, with the online technologies enabling contemporary esports
also facilitating mass participation in previously localised practices (Scholz, 2011).
There are, for example, emergent forms of in-game gambling in which in-game virtual
items and currencies are used as stakes in-game events ultimately determined by random
number generators. Furthermore, the online streaming of competitive video game play
means that established gambling activities, such as sportsbook-style betting, can be
transposed to this new arena (Macey and Hamari, 2019).
Recent years have seen the creation of a number of gambling activities directly asso-
ciated with computer games. This includes those which have emerged from within the
gaming community, such as skins lotteries and crash betting (Macey and Hamari, 2018,
2019), and those used to drive monetisation of games, such as loot boxes (Hamari and
Lehdonvirta, 2010; King and Delfabbro, 2019). Concerns raised about the use of virtual
items mean that the market is in a state of flux and that estimates of its size are constantly
being revised. A further complicating factor is the fact that many sites offering gambling
activities related to video games are not licenced. Indeed, there is an ongoing debate as
to whether or not many of these activities can even be considered gambling and, conse-
quently, whether or not they are subject to regulation (Abarbanel, 2018; Holden and
Ehrlich, 2017; Macey and Hamari, 2019).
As the popularity of esports has grown, many established gambling operators have
begun to offer sports books on esports events (Dos Reis, 2017). As a result, the size of
the esports-related gambling market can be estimated with much greater confidence. The
annual esports gambling market is estimated to be worth between US$2.3 billion (Eilers
& Krejcik, 2018) and US$50 billion (Juniper Research, 2018), a significant increase in
the size of the esports market itself, which in 2018 was valued between US$800 million
(PwC, 2019) and US$869 million (Goldman Sachs, 2018). It is important, however, to
maintain a sense of caution when considering such estimates as the underlying method-
ology is typically opaque in nature and may be used to further a specific agenda, such as
Macey et al. 3
Given the continued development of esports, ever-increasing prize-pools and an
expanding consumer base, the already significant gambling market is also likely to con-
tinue growing. As such, it is important to understand how individuals’ habits of gaming,
gambling and consuming esports as well as demographic factors are associated with
participation in esports betting. Many esports gambling opportunities are inextricably
tied to video games, including both play and spectatorship activities. For example, player
versus player (PvP) betting, in which video gamers can bet against one another based on
their own performance, is growing in popularity (Grove, 2016). Gambling industry spon-
sorship of esports events, meanwhile, provides increasing exposure of gambling activi-
ties to esports spectators (Luongo, 2018). With this in mind, it is important to establish a
holistic picture of the ways in which esports betting is associated with the consumption
habits of media directly connected to the practice in order to understand how they inter-
act with one another. This approach, therefore, lays the groundwork for further studies
investigating these newly emergent practices and their relationships with existing behav-
iours. As such, this research is guided by the following question:
RQ: How are demographic characteristics and the consumption of video game-
related media (video games, esports and gambling) associated with esports betting
This research will provide an overview of the changing ways in which video games are
being consumed, both in the emergence of esports and of the betting activities associated
therewith. Subsequently, this article outlines the hypothesised relationships between
demographic characteristics, media consumption practices and esports betting practices
before describing the research model employed in this study. After outlining the meth-
ods, measures, participants and procedures this article presents, the results of the study in
reference to demographic characteristics and measures of consumption. The findings are
discussed alongside their theoretical and practical implications, potential avenues of
future research, and the limitations of this work.
This research will thus contribute to the growing body of literature related to the con-
vergence of gambling and (video) gaming. Specifically, this study investigates the inter-
relations between the motivations for consuming esports, consumption of digital media
products associated with esports and participation in esports betting. As such, this work
will provide evidence as to whether esports betting replicates relationships present in tra-
ditional sports betting, or if this emergent activity is accompanied by novel relationships.
The consumption of video games as sports
The emergence of arcade gaming has been presented as a key point marking the shift
from the traditional, Fordist, approach to capitalism to a post-modern approach based
around the commodification of experiences (Borowy and Jin, 2013). This early period of
esports, as it is now known, combined the approach of traditional sporting events, tech-
nology and the marketing of experience as a commodity in itself. The scope of these
4 new media & society 00(0)
experiences ranged from watching celebrity players compete in local tournaments, to the
showcase performances of the US National Video Game Team at events across the coun-
try. However, the transition to mainstream acceptance of arcade gaming as a sport seems
to have been hampered by the constraints of the technology at the time; head-to-head
competition between players was not possible, with performances instead being meas-
ured by high score (Borowy and Jin, 2013).
It was only with the introduction of Local Area Networks, and associated technolo-
gies, that competitive video gaming could move away from the player-versus-machine
dynamic towards one characterised by PvP interactions (Griffiths et al., 2003). In this
way, competitive video gaming could realistically be conceptualised as constituting a
This trend continued with the emergence of IPTV (Scholz, 2011) and streaming tech-
nologies (Hamilton et al., 2014; Hamari and Sjöblom, 2017) which have been credited
with the rise of online communities centred around user-generated content. Such envi-
ronments mean that developing and maintaining a community centred around esports has
become much more feasible with contemporary consumption of esports taking place in a
‘mediascape’ of blogs, streams, podcasts and on-demand video (Taylor, 2012). Indeed,
the development of streaming has facilitated and promoted both the consumption of
esports and, in the wider context, of video game play as a media product in itself. Previous
works have highlighted the ways in which esports has enabled gaming culture to move
from the private domain into the public, and the new socio-technological relationships
that this has engendered (Johnson and Woodcock, 2017; Taylor, 2018). Further research
has examined the interactions between the consumers and the producers of streamed
content, whether this be in regard to underlying motivations for consumption (Sjöblom
and Hamari, 2017), or the changing dimensions of such shared experiences (Scully-
Blaker et al., 2017).
The development of video game play as an entertainment product highlights the
fact that online media constitute the basis upon which contemporary esports is built;
facilitating large-scale consumption through online platforms and paving the way for
the subsequent involvement of mainstream broadcast media. In this way, the devel-
opment of esports can be seen to mirror that of traditional sports, where the introduc-
tion of mass media technologies was an event of huge historical significance (Carter
and Gibbs, 2013; McChesney, 2008). The popularity of contemporary media services
providing the means to spectate esports is, therefore, a natural and predictable
Modern live esports events often attract tens of thousands of attendees, sometimes
even reaching over 100,000 spectators (ESL, 2019; Needleman, 2015; Taylor, 2016).
The act of consuming video games simply as a spectator, rather than a player, or as both
a player and spectator, is a problematic concept for many in wider society, where notions
of consumption are focused on the interaction between the player and the game. However,
both spectating and playing video games present aspects of a single spectrum rather than
existing as distinct, binary states (Taylor and Witkowski, 2010). Previous work has also
established the diversity of roles present in contemporary consumption practices associ-
ated with video games, revealing that there are many associated behaviours which also
require attention (Seo and Jung, 2016).
Macey et al. 5
Despite an ongoing debate within Game Studies concerning the nature of audience in
relation to an actional, rather than passive media, spectating play has always been a fun-
damental aspect of the gaming experience (Taylor, 2016) and of other forms of play,
including sports (Carter and Gibbs, 2013; Sutton-Smith, 2009). The role of technology
and media in the popularisation of esports has been likened to that of traditional sports
due to the way in which technological advances have facilitated mass consumption
through new media (Carter and Gibbs, 2013).
A consistent theme within the scientific literature on esports has been the location
of the activity in reference to established concepts of sport (Cheung and Huang, 2011;
Jenny et al., 2017; Witkowski, 2012). Discussions have focused on defining esports,
documenting it as a cultural phenomenon (Karhulahti, 2016; Taylor, 2012) and posi-
tioning the practice in relation to both traditional sports and to video games (Carter and
Gibbs, 2013; Hamari and Sjöblom, 2017; Jonasson and Thiborg, 2010; Witkowski,
The study of digital play, and players, in physical environments has continued as
esports has developed (Taylor, 2016; Taylor et al., 2014), while another consistent theme
has been the motivations underlying esports consumption (Hamari and Sjöblom, 2017;
Lee and Schoenstedt, 2011; Weiss and Schiele, 2013). Exploratory studies, such as that
by Cohen and Avrahami (2005) have shown that measures designed for assessing sports
in general, such as the Sports Fan Motivation Scale (SFMS: Wann, 1995) and the
Motivation Scale for Sports Consumption (MSSC: Trail, 2012; Trail and James, 2001),
can be applied to specific types of sport and in different cultural contexts. In addition,
they can be used to differentiate between attendance at live events, ‘active participation’,
and watching at home, ‘passive participation’ (Cohen and Avrahami, 2005).
With this in mind, the use of such measures to assess motivations for consuming
esports is a natural and logical step (Hamari and Sjöblom, 2017; Lee and Schoenstedt,
2011), and early esports studies have revealed that spectators share many of the same
motivations as traditional sports fans (Cheung and Huang, 2011).
Research into sports spectatorship and gambling motivational dimensions demon-
strates a clear relationship. For some sports spectators, for example, gambling serves as
a means of adding excitement to the spectating experience (Nelson et al., 2012; Petry,
2003). For others, it is the potential financial gains that drive a person’s gambling during
sports spectatorship (Wann, 1995). Gambling research has found similar motivational
dimensions for sports betting (Abarbanel, 2014; Challet-Bouju et al., 2014; Flack and
The MSSC (Trail and James, 2001) includes a series of constructs that parallel motives
for gambling, such as ‘vicarious achievement’, ‘acquisition of knowledge’, ‘drama’ and
‘escape’. The MSSC itself has also been shown to be associated with increased gambling
behaviour, with individual sub-scales exhibiting varying degrees of influence (Karg and
McDonald, 2009; Lopez-Gonzalez et al., 2018).
The MSSC was selected as the measurement instrument for this research as, like the
SFMS, it has been found to be an appropriate measure for investigating the motivational
drivers of sports consumption across different sports and contexts. However, unlike the
SFMS, the MSSC has been used to investigate the motivations underlying the consump-
tion of both esports and gambling, meaning that it is more likely to constitute an
6 new media & society 00(0)
appropriate measure for investigating esports betting. For a full discussion of extant
measures assessing motivations for sports consumption, see Hamari and Sjöblom (2017).
Esports and betting
For purposes of this article, esports betting refers to wagering on any type of esports or
video gaming event, irrespective of currency used (e.g. fiat currency, skins), licenced
versus offshore site, and professional versus amateur competition. Furthermore, this arti-
cle specifically investigates wagering behaviours and preferences as they relate to esports
events and competition, and not gambling specific to video game play (e.g. loot boxes,
casino/themed games in video games, in-game PvP gambling activities, or skins betting).
While these gambling phenomena are all tied to video games, a focus on wagering paral-
lels research of behaviours that centre upon the spectatorship of competition.
This focus on esports betting is also seen with traditional bookmakers, who are
increasingly establishing esports markets within their offerings and sponsoring major
esports events (Byrne, 2019). Meanwhile, the relationship between gambling and esports
is a complex one. In esports, there are ongoing debates on the relationship between
esports and sports, particularly in how the terms are defined (Jenny et al., 2017). This has
a particular impact on betting markets, as many jurisdictions differentiate games, events
and sports under different regulatory structures (Owens, 2016). And the rapid growth of
esports, combined with its grassroots nature, does not exist within the same cohesive
governance that is present for many sports (e.g. Fédération Internationale de Football
Association for football, or the National Basketball Association for basketball; Dos Reis,
2017). Thus, potential game integrity issues (such as match-fixing or other forms of
cheating) threaten gambling market integrity needs, and esports spectators do not always
recognise the severity of integrity issues (Abarbanel and Johnson, 2019). While a signifi-
cant portion of the esports betting market is still conducted in the opaque offshore mar-
kets (Eilers & Krejcik, 2018; Juniper Research, 2018), there is now a burgeoning field of
research into esports betting behaviours, establishing a foundation for further research.
Early research in the field found that US esports fans were twice as likely to have
gambled online than the average US-based Internet user. In addition, one-third of US
esports spectators had gambled (measured across all gambling games) more than a few
times per week in the prior year (Newzoo, 2016). We note, however, that these findings
were published by market researchers and must be viewed with caution due to the lack
of methodological transparency. However, given the lack of comparable academic
research, they provide an indication of gambling habits in the contemporary esports envi-
ronment. In another early survey of US esports bettors, Grove (2016) found that esports
event wagering was the dominant form of gambling, followed by casino-style wagers
using virtual items from video games (e.g. skins). A later study used a global reach, find-
ing that esports bettors typically placed wagers on two different sites, with the most
popular sites being traditional bookmakers (Grove and Abarbanel, 2016).
Existing research has shown that betting on traditional sports is influenced by both
experiential and economic motives (Humphreys et al., 2013). Many of the same
motives that influence fan spectatorship also influence sports bettors, such as closely
matched games between high-quality opponents (Humphreys et al., 2013). Recent
Macey et al. 7
market research has also found that the ability to bet on sports drives TV spectator-
ship, with sports bettors most interested in placing wagers on championship games
and teams they follow (Bridge, 2019).
Esports bettors have been found to have higher involvement in gambling than sports
bettors, demonstrating higher gambling involvement (e.g. higher frequency of play,
greater number of games and platforms used), and are more likely to use unlicensed
gambling sites (Gainsbury et al., 2017).
A 2017 report from the UK Gambling Commission estimated that 58% of esports
bettors were men, and the predominant age group for esports betting was 25 years to
34 years (Gambling Commission, 2017). It is of note, however, that this report did not
include adolescent respondents. A 2018 UK Gambling Commission study on youth
gambling behaviour found that 3% had placed wagers using skins acquired from com-
puter or app games, though the specific form of wagering was not specified (Gambling
Finally, research into associations between video gaming and gambling behaviours
has produced mixed results. While several studies have found significant relationships
between video gaming and gambling (Gainsbury et al., 2016; Kim et al., 2014), others
have found that gambling may not be particularly associated with video game consump-
tion (King et al., 2012; Forrest et al., 2016).
Macey and Hamari (2018) investigated the relationship between video gaming
behaviours, esports spectatorship behaviours, and gambling behaviours, with a focus
on problematic gambling. They found that esports spectatorship (measured by fre-
quency of spectatorship, time and monetary spend) was associated with increased
online and video game related gambling. Subsequent research builds upon this, finding
that betting is the most popular online gambling activity among esports spectators
(Macey and Hamari, 2019).
The research described earlier, justifies the formulation of a research model that
includes interactions between esports spectatorship motivations, demographic character-
istics, consumption of digital media and participation in established forms of gambling.
The research model
Stemming from the earlier discussion, the research model of this study is operationalised
to investigate how individuals’ consumption of video games, esports and gambling, in
addition to demographic factors, are associated with esports betting behaviour. Moreover,
as the motivations of esports spectating are pertinent to both esports consumption and
esports betting, the model also investigates its association with the esports consumption
and esports. This research utilises an involvement model (Binde, 2013) as, while both
motivational factors and gambling involvement variables are included, the latter are
Consistent with the discussion in the ‘Background’ section, we hypothesise that the
MSSC will be positively associated with the consumption of esports (H1), esports bet-
ting (H2) and the use of dedicated esports betting sites (H3).
In addition to the established relationship between sport consumption and gambling,
previous research has shown that increased engagement with esports is associated with
8 new media & society 00(0)
increased gambling connected to esports (Macey and Hamari, 2018). Therefore, the con-
sumption of esports is expected to be positively correlated with both esports betting (H4)
and the use of dedicated esports betting sites (H5).
Previous research has also shown that the spectating of esports has been associated
with young males (Hamari and Sjöblom, 2017; Macey and Hamari, 2019), above aver-
age levels of educational attainment and household income (PwC, 2016). As such, the
consumption of esports is expected to negatively correlate with age and to be associated
with males, higher levels of education and higher levels of household income (H6).
The consumption of video games has been increasing as wider cultural acceptance of
gaming has spread (Kuo et al., 2017; Muriel and Crawford, 2018) and, despite increasing
numbers of women playing games, existing research has shown that it is positively asso-
ciated with young males located in urban areas and with access to newer technologies
(Borowiecki and Prieto-Rodriguez, 2015). The widespread consumption of video games
– 60% of Americans play video games daily, with almost every household having a dedi-
cated gaming device (ESA, 2018) – suggest that although game play is associated with
younger males, it is unlikely to correlate with other demographics (H7).
Consumers of video games in general, and esports in particular, are younger than
average demographic (Borowiecki and Prieto-Rodriguez, 2015; Seo, 2013), while gam-
bling activities associated with these media are almost exclusively facilitated online
(Macey and Hamari, 2018, 2019). As such, demographic characteristics associated with
gambling consumption are likely to mirror those of (predominantly) online gamblers,
rather than traditional profiles (H8), as seen in the work of Gainsbury et al. (2017).
Due to the prevalence of esports betting in the online context (Macey and Hamari,
2019), it is expected that esports betting participants will display the following similar
characteristics: younger males, higher levels of education and household income (H9). It
is not expected that any correlation will be found in regard to marital status. As the use
of dedicated betting sites is dependent upon actual participation in esports betting activi-
ties, it is expected that the same demographic characteristics will be correlated with the
use of dedicated sites (H10).
Esports is fundamentally characterised as competitive video game play (Hamari and
Sjöblom, 2017). Therefore, it is expected that increased consumption of video games
will also be positively associated with increased betting on esports (H11), as has been
found in previous research (Macey and Hamari, 2018).
Previous works have also shown that as gambling involvement grows, the number of
different activities and channels of participation also grows (Gainsbury et al., 2012;
Macey and Hamari, 2018). As such, it is expected that increased participation in general
forms of gambling will be reflected in increased esports betting (H12).
The path model used to investigate the research question stated earlier is presented
below (Figure 1).
A survey was used to collect data, with participants recruited from an online panel main-
tained by the market research company Qualtrics. The survey remained open during the
period 11–19 April 2018. Due to the nature of the research, the following inclusion
Macey et al. 9
criteria were stipulated that participants be aged 18 years or older and that they had
played video games or watched esports at least once in the previous 12 months. The
principles of informed consent were followed, with potential participants being advised
that participation was entirely voluntary and that it could be withdrawn at any time. The
informed consent document notified respondents that the survey was about video games,
game play, spectating and gambling. Participants were required to sign a consent form
prior to accessing the survey. No incentive was provided for completing the survey.
Ethical approval for this study was provided by the Institutional Review Board at
(University blinded for review).
A total of 2035 responses were received, 400 incomplete responses were removed,
and a further 230 were removed as they did not meet the inclusion criteria. A total of 37
univariate and multivariate outliers were also removed, resulting in a finalised dataset of
1368 records. Participants were asked to complete items measuring the following demo-
graphic information: Age, Gender, Marital Status, Annual Household Income and
Educational Attainment. Age was recorded as a continuous variable, meaning there were
no pre-defined brackets or ranges that could be selected. Both Gender and Marital Status
were nominal items, with the following response options: male, female, other/non-
binary; single, married, unmarried (cohabiting), separated, divorced, widowed, other.
Figure 1. Research model.
10 new media & society 00(0)
Annual Household Income and Educational Attainment were ordinal variables, response
options were: from ‘under US$20,000’ to ‘over US$1,000,000’; and from ‘Less than
High School/Secondary/Equivalent’ to ‘Graduate Degree’.
In addition to the demographic information listed earlier, the survey included items
measuring the consumption of video games, esports and gambling activities. Motivations
for consuming esports content were also collected through the inclusion of an esports-
adapted MSSC (Trail and James, 2001). This research employs the updated version of
the MSSC (Trail, 2012), a previously validated scale used in general terms and in refer-
ence to specific sports from Wrestling (Schaeperkoetter et al., 2016) to South African
soccer (Stander and van Zyl, 2016). It has also been adapted for use in a wide range of
sporting contexts, such as disability sports (Cottingham et al., 2014) and esports (Hamari
and Sjöblom, 2017). The MSSC is a 31-item measure, with items being rated on a five-
point Likert-type scale, possible responses range from ‘strongly disagree’ (1) to ‘strongly
agree’ (5). The scale utilises 10 sub-constructs to assess consumer motivations and has
been designed for use in multiple contexts. In order to reflect the focus of this research,
‘esports’ was inserted in the relevant fields throughout the scale, as per the manual (Trail,
2012). An example of an updated item is ‘An individual player’s “sex appeal” is a big
reason why I watch esports’. In the structural model here, MSSC will be utilised as a
single latent variable, rather than 10 distinct constructs. A Cronbach’s Alpha value of
.956 established the internal consistency of the scale.
The consumption of video games was assessed using a formative variable, Video
Game Consumption, consisting of items measuring frequency of video game play, aver-
age hours spent per gaming session and the social context of game play. All questions
were asked in reference to video game play habits over the preceding 12 months.
In addition to video games, the model also included items that constituted the inde-
pendent variable Esports Consumption. As with any sporting activity, consumption can
take the form of spectating or participating. As this research was concerned solely with
spectating behaviours, all items explicitly asked respondents to consider the questions in
respect to watching esports. Similar to Video Game Consumption, Esports Consumption
utilised a formative variable consisting of several distinct aspects: prior year frequency
of watching esports, average hours spent watching esports per session, the social context
of watching esports and the type of esports broadcasts (live or pre-recorded) consumed.
Regarding the independent variable Gambling Consumption, participants were asked
to provide information regarding their participation in gambling activities in the previous
12 months, no distinction was made between different forms of gambling (online versus
offline, for example). Once again, consumption habits were assessed using a formative
variable that included the following items: frequency of gambling, average hours spent
per gambling session, and average dollar spend per gambling session.
Finally, the model included two dependent variables to specifically measure esports
betting behaviour. The first, Esports Bet asked whether participants had wagered money
on the outcome of an esports event in the past year, response options were yes, no and I
cannot remember. The second dependent variable, Esports Bet–Dedicated Site, is an
Macey et al. 11
ordinal variable measuring whether participants placed wagers through dedicated esports
betting sites (e.g. Unikrn), general sportsbook providers (e.g. bet365), or both.
This study employs Structural Equating Modelling as the statistical techniques for
analysing the data. SEM is a combination confirmatory factor analysis and multiple
linear regression. In particular, we employ Partial Least Squares-Structural Equation
Modelling (PLS-SEM analysed with SmartPLS 3 software package) which uses an
iterative approach for maximising the explained variance of endogenous constructs,
using a combination of multiple linear regression and confirmatory factor analysis, and
more efficiently addressing the issue of multicollinearity in regression problems
(Fornell and Bookstein, 1982; Wold et al., 1984). PLS-SEM is advisable when the
model includes a combination of both formative and reflective latent variables and
where the focus is on prediction rather than in trying to established the most fitting
model (Chin et al., 2003; Hair et al., 2016). Descriptive statistics were produced using
SPSS version 24 for Windows.
Established methods for assessing validity and reliability are based on reflective con-
structs. However, the specified research model utilises formative constructs to measure
consumption habits, meaning that standard practices are not applicable (Diamantopoulos
and Winklhofer, 2001; Wang et al., 2015). Construct validity is thus established here
using assessment of Variance Inflation Factors (VIFs). All VIF values except 1, were
under 3, with the largest VIF value still under the standard threshold of 5, indicating that
collinearity was not present and meaning that the constructs used were robust
(Diamantopoulos and Siguaw, 2006; Hair et al., 2016). In the MSSC variable, 27 of the
30 items have outer VIF values lower than 3, all were under the threshold of 5.
Participants ranged from 18 years to 80 years of age (M = 37.83), with the majority
reporting their gender as male (58.4%). Participants reported being either single or mar-
ried at approximately equal rates, 35.1% and 37.9%, respectively, the majority (56.9%)
earned less than US$50,000 per year per household, with a minority having completed a
2-year college/university degree or higher qualification (37.8%). Full details of demo-
graphic statistics are provided in Online Appendices A to E, with a summary table pro-
vided in Online Appendix F. Participants were overwhelmingly from the United States
(N = 1152; 97.9% of those who provided their nationality). The data sample included a
further 21 nationalities, of which none totalled more than 0.2% of the sample.
The mean age in the sample is higher than in some similar studies investigating gam-
bling, video game play, and spectatorship (e.g. Macey and Hamari, 2018, 2019), but is
in line with others (e.g. Gainsbury et al., 2017). This sample is also more balanced in
gender distribution (recent studies have ranged from 62%–91% male, for example), but
represents lower income and education levels. Given the relative youth of this field,
however, we note that there is not currently a baseline for what constitutes a truly rep-
12 new media & society 00(0)
The majority of participants (68.4%) reported playing video games at least twice a week
or more, with average play sessions of up to 2 hours (57%). The mean length of play
sessions was 3.92 hours (Table 1).
Almost half (47.5%) of the participants reported watching esports, of whom 47.3%
reported watching twice a week or more. Esports spectating mirrored video game con-
sumption with the median average session length being 2 hours. The majority of respond-
ents (58%) reported average spectating sessions of up to 2 hours, and the mean duration
of sessions spent watching esports was 3.94 hours (Table 2).
Table 1. Media consumption frequencies.
Play video games Watch esports
Count % Count %
Never 37 2.7 718 52.5
Less than once per month 61 4.5 81 5.9
1–3 times per month 189 13.8 187 13.7
Once per week 134 9.8 90 6.6
2 times or more per week 936 68.4 272 19.9
Total 1357 99.2 1348 98.5
Missing 11 0.8 20 1.5
Total 1368 100.0 1368 100.0
Table 2. Media consumption–average hours per session.
Play video games Watch esports
Count % Count %
upto 1 hour 21 1.5 21 1.5
1 < 2 hours 323 23.6 193 14.1
2 < 3 hours 298 21.8 152 11.1
3 < 4 hours 142 10.4 94 6.9
4 < 5 hours 103 7.5 45 3.3
5 < 10 hours 137 10.1 64 4.6
10 < 15 hours 40 2.9 30 2.2
15 < 20 hours 15 1.1 7 0.5
20 < 25 hours 29 2.1 12 0.9
25 < 30 hours 5 0.4 3 0.2
30 < 35 hours 3 0.2 4 0.3
35 < 40 hours 8 0.6 1 0.1
40–45 hours – – 5 0.4
Missing 244 17.8 737 53.9
Total 1368 100.0 1368 100
Macey et al. 13
In total, 718 respondents reported playing video games but not watching esports (52.5%),
37 reported watching esports but not playing video games (2.7%), and 613 (44.8%) reported
both playing video games and watching esports within the previous 12 months.
The majority (52.1%) of respondents reported gambling at least once within the previ-
ous 12 months, however, a notable minority (approximately 13.5%) gambled once a
week or more. Most participants reported average length of gambling sessions of up to 2
hours (55.3%, median: 2 hours). The mean length of gambling sessions was 7.06 hours.
Participants reported spending between US$0 and US$5000 per session, with median
spend at US$40 and mean spend at US$108.27 (Table 3). Online Appendix G shows
reported gambling frequencies.
For the purposes of analysis, participants who answered ‘I can’t remember’ for the
Esports Bet item were coded as non-bettors. Of those who reported betting on esports,
an overwhelming majority reported using only dedicated esports betting sites (71.74%),
with a further 13.77% using both dedicated and general betting sites.
Figure 2 shows the total effects for the research model. For purposes of clarity, only
statistically significant relationships are displayed. A table detailing all direct and indi-
rect effects is provided below in Table 4.
The MSSC was found to positively correlate with esports consumption, as stated
in H1, however, the path coefficient can be considered weak, β = .187, p < .001
Table 3. Average Spend Per Gambling Session.
Dollars ($) Time (hours)
Count % Count %
upto US$1 23 1.7 upto 1 hour 39 2.9
US$1 14 1.0 1 < 2 hours 165 12.1
US$2 12 0.9 2 < 3 hours 169 12.3
US$3 6 0.4 3 < 4 hours 101 7.3
US$4 0 0 4 < 5 hours 54 3.9
US$5–US$9 42 3.1 5 < 10 hours 59 4.3
US$10–US$14 77 5.6 10 < 15 hours 19 1.4
US$15–US$19 9 0.6 15 < 20 hours 3 0.2
US$20–US$29 125 9.1 20 < 25 hours 20 1.5
US$30–US$49 45 3.3 25 < 30 hours 4 0.3
US$50–US$99 103 7.5 30 < 35 hours 7 0.5
US$100–US$149 115 8.4 35 < 40 hours 4 0.3
US$150–US$199 15 1.1 40 < 50 hours 2 0.1
US$200–US$299 37 2.7 50 to 100 hours 30 2.2
US$300–US$399 13 1 Missing 694 50.7
US$400–US$499 1 0.1 Total 1368 100
US$500–US$999 21 1.5
US$1000–US$5000 19 1.4
Missing 692 50.6
Total 1368 100
14 new media & society 00(0)
(Cohen, 1988). Both H2 and H3 were also supported, as the MSSC was found to posi-
tively correlate with both esports betting and the use of dedicated sites, albeit with weak
overall effects, (β = .174, p < .001 and β = .138, p < .001, respectively). The consumption
of esports was also found to have positive correlations, of moderate strength, with both
esports betting and the use of dedicated esports betting sites (β = .268, p < .001 and
β = .250, p < .001, respectively), supporting both H4 and H5.
While the consumption of esports was found to be associated with younger partici-
pants (β = −.260, p < .001) and male gender (β = −.163, p < .001), no statistically signifi-
cant relationships were observed with respect to marital status, annual household income,
or highest level of educational attainment, in partial support of H6. As predicted, the
consumption of video games was also associated with younger males, but no other
demographic characteristics (H7).
Of all demographics, only gender was found to have a statistically significant rela-
tionship with the general consumption of gambling activities, meaning that H8 was
unsupported: Gender - > Gambling Consumption β = −.145, p < .001.
As with H6, participation in esports betting was associated with younger males
(β = −.148, p < .001 and β = −.105, p < .001, respectively), but no other demographic
measure. Therefore, H9 was partially supported. H10 was supported, as the relationship
between esports betting and demographic characteristics was replicated, almost exactly,
in the use of dedicated esports betting sites.
Contrary to expectations, no statistically significant relationships were observed
between the consumption of video games and esports betting activity, meaning H11 was
Finally, H12 was supported, with increased participation in general forms of gam-
bling positively associated with increased betting on esports and the use of dedicated
sites (β = .241, p < .001 and β = .199, p < .001, respectively).
Figure 2. Path model showing total effects, significant relationships only.
*** p = < .001.
Macey et al. 15
Table 4. Direct and total effects.
βT Stats pβT Stats p
Age -> Esports bet –.057 2.521 .012* –.148 6.692 <.001***
Age -> Esports bet
–.036 1.575 .115 –.121 5.573 <.001***
Age -> Esports
–.257 9.91 <.001*** –same as direct–
Age -> Gambling
–.024 .8 .424 –same as direct–
Age -> Game
–.295 10.428 <.001*** –same as direct–
-> Esports bet
.269 7.953 <.001*** –same as direct–
-> Esports bet
.249 7.928 <.001*** –same as direct–
-> Esports bet
.239 7.843 <.001*** –same as direct–
-> Esports bet
.198 6.326 <.001*** –same as direct–
.052 1.813 .07 –same as direct–
-> Esports bet
.055 1.833 .067 –same as direct–
Gender -> Esports bet –.021 0.953 .341 –.105 4.534 <.001***
Gender -> Esports
bet dedicated sites
–.026 1.168 .243 –.1 4.538 <.001***
Gender -> Esports
–.159 6.437 <.001*** –same as direct–
Gender -> Gambling
–.145 5.808 <.001*** –same as direct–
Gender -> Game
–.111 4.276 <.001*** –same as direct–
-> Esports bet
.02 0.836 .403 .023 0.842 .4
-> Esports bet
.019 0.818 .413 .021 0.819 .413
Highest education ->
.002 0.063 .95 –same as direct–
Highest education ->
.016 0.512 .609 –same as direct–
16 new media & society 00(0)
βT Stats pβT Stats p
-> Game consumption
–.028 1.001 .317 –same as direct–
-> Esports bet
.008 0.341 .733 .03 1.158 .247
-> Esports bet
.004 0.154 .877 .022 0.926 .355
-> Esports consumption
.031 1.139 .255 –same as direct–
.053 1.775 .076 –same as direct–
-> Game consumption
.007 0.24 .811 –same as direct–
MSSC -> Esports bet .122 4.254 <.001*** .174 6.721 <.001***
MSSC -> Esports
bet dedicated sites
.089 2.926 .003** .137 4.94 <.001***
MSSC -> Esports
.193 5.255 <.001*** –same as direct–
-> Esports bet
.019 0.78 .435 .008 0.315 .753
-> Esports bet
.01 0.368 .713 .001 0.019 .985
Marital status ->
–.02 0.667 .505 –same as direct–
Marital status ->
–.025 0.807 .419 –same as direct–
Marital status ->
.016 .539 .59 –same as direct–
MSSC: Motivation Scale for Sports Consumption.
*p < .05; **p < .01; ***p < .001.
Table 4. (Continued)
Investigating relationships between the use of digital media associated with video
games and gambling activities has revealed that as consumption of esports and general
gambling increases, so does esports betting. However, consumption of video games
was not associated with increased betting on esports. In addition, a MSSC motivations
adapted for use in esports shows only weak predictive power in this context, while also
demonstrating small, but statistically significant, associations with esports betting
activity. The MSSC was positively associated with the consumption of esports (H1),
Macey et al. 17
betting on esports (H2) and the use of dedicated esports betting sites (H3), meaning all
three hypotheses are supported. However, the path coefficients were weak in magni-
tude, despite previous works finding that the MSSC is a good predictor of both sports
consumption and sports gambling participation (Karg and McDonald, 2009; Lopez-
Gonzalez et al., 2018; Trail and James, 2001). As such, it may not be the optimal
measure for assessing motivations underlying esports consumption. This is further
supported by the findings of previous studies which show that only a limited number
of MSSC constructs exhibit statistically significant relationships in the context of
esports consumption (Hamari and Sjöblom, 2017).
The finding that consumption of esports positively correlates with betting on esports
(H4) mirrors established practices in traditional sports betting; increased consumption
serves to build the knowledge base utilised in sports betting. In addition, there is ample
evidence of sports fans demonstrating sentiment bias by betting on a positive result for
their favoured team (Feddersen et al., 2017). Somewhat counter-intuitively, the reverse
is also true. Some studies have found that fans may bet against their own team in order
to lessen the blow of a negative result, a practice known as ‘hedging’ (Agha and Tyler,
2017). The concept of fandom may be a particularly strong driver for betting in the
context of esports due to its robust and vibrant community, also potentially explaining
the preference for the use of dedicated esports betting websites (H5), with many of
these sites developed from within the community. This is in contrast to established
sports betting companies, who may have only recently added esports lines to their
books. As those who spectate esports are more familiar with the games, they may
potentially look to sites that are specifically focused on these games, rather than a site
that is designed for more general gambling/sports betting. Such behaviours support the
perspective that esports consumers are more than simply players or spectators, and that
there are numerous interrelated practices associated with the consumption of video
game play (Seo and Jung, 2016).
The fact that only age and gender demographic items have statistically significant
relationships with the consumption of esports (H6) serves to confirm the findings of
previous research (Gainsbury et al., 2017; Hamari and Sjöblom, 2017; Macey and
Hamari, 2019). Similarly, the consumption of video games is only associated with
younger consumers (H7). These results tell us that consumption of media related to
video games is becoming more mainstream as its reach extends across nearly all socio-
economic markers, something which has been well documented by both academia and
market research organisations.
The relationships of age and gender with consumption measures (H6 and H7) appears
to confirm results of previous research, in that they suggest a stronger association with
the consumption of esports and video games than that which is presented by market
research organisations. It may be that this is a result of the eligibility requirements for
this survey (participants qualified if they had gamed or gambled in the prior 12 months),
but as other studies have had different criteria for inclusion it is unlikely.
Given that previous works have found that esports bettors are similar in demographic
makeup to early adopters of online betting (Gainsbury et al., 2017), it was somewhat
surprising that similar characteristics were not present in this study. It may be that as the
consumption of digital media associated with both video games and esports becomes
18 new media & society 00(0)
ever more widespread, socio-economic distinctions are becoming less apparent, as dis-
The results of H9 and H10 conform to existing knowledge concerning participation in
sports betting. Increased participation is associated with males, although esports betting
has a less pronounced division than traditional sports betting (Gainsbury et al., 2017). In
addition, it confirms that there is a significant, and fairly robust, association between the
consumption of esports and wagering on esports events, as discussed in the ‘Background’
Increased consumption of video games was expected to be associated with increased
participation in esports betting activity (H11), however, no statistically significant rela-
tionships were observed. That the p values were in the region of .07 suggests that this
finding may just be a characteristic of the data sample employed in this research, and as
such it is worthy of further investigation. Conversely, it may be that games simply act as
a mediator for esports betting, a relationship observed in previous research. In addition,
this study looked at all forms of gambling related to video games, not solely esports bet-
ting (Macey and Hamari, 2018).
The statistically significant relationship between increased consumption other forms
of gambling with betting on esports (H12) also reinforces findings from previous works
(Gainsbury et al., 2012; Macey and Hamari, 2018). We can see, therefore, that the emer-
gence of gambling activities associated with esports is neither novel, nor unexpected.
The findings of H1-3 suggest that the MSSC may not be the most appropriate measure
for assessing motivational drivers of esports consumption. As such, it feeds into the
ongoing discussion concerning the equivalence of esports to traditional sports (Jenny
et al., 2017) and, while the competitive nature of esports is undeniable, it may be that the
computer-mediated context of consumption fulfils different motivational needs for
An avenue for future study would be the assessment of the MSSC in the context of
esports consumption. Indeed, the field would benefit from such work in relation to all
extant measures. Such work would establish whether any existing scales are valid meas-
ures for esports, or if the development of a dedicated scale is required. Given the highly
mediated nature of esports consumption, it may also be that motivations differ between
online consumption and attendance at live events.
This research supports previous works that found stronger associations between the
consumption of video games, spectating esports, gender, and age, than those presented in
published market research and discussed in the ‘Background’ section of this work.
Therefore, a valuable direction for future work would be to continue to build on con-
sumer studies in order to establish a reliable picture of contemporary media consumers
by market segment (e.g. video games, esports, other streams).
Given the established findings that betting appears to be a significant aspect of
engaged esports fandom, it is no surprise to see similar relationships present in this sam-
ple. A potential avenue for future work could be to understand whether this behaviour is
derived from similar motivations to traditional sports (e.g. Vicarious Achievement,
Macey et al. 19
Drama, etc.) or as the result of video game consumption (e.g. self-perception of increased
skill development leading to a preference for skill games rather than chance games).
Finally, the findings associated with H11 lend weight to a growing body of work that
questions the traditional position that video game play is associated with increased par-
ticipation in gambling (Delfabbro et al., 2009; Forrest et al., 2016).
The most significant limitation of this study was the use of a questionnaire distributed to
an online panel. Participants are self-selected, and this form of recruitment may over-
sample higher games, spectating and gambling involvement, particularly considering
that the survey specifically sought those who had participated in video games or esports.
As such, the results may not reflect the wider population and, consequently, lack gener-
alisability. The limitations of survey-based research, indeed any form of data collection
which relies of self-reported data, also extend to the potential for responses to be influ-
enced by the participants’ desire to be perceived favourably, or through inaccurate recol-
lection. However, the use of a third-party organisation to recruit participants may also
reduce the potential for self-selection bias to affect results. Indeed, using a third-party
organisation in this case resulted in a sample that was more representative of wider soci-
ety than many other recent works in the field.
The primary aim of this research is to investigate the ways in which the consumption
of esports video content, video game play and gambling activities are related to partici-
pation in esports betting. As such, the eligibility criteria for participants were that they
had played video games and/or watched esports within the prior 12 months. With this in
mind, results here may not be applicable to people who bet on esports, but do not watch
esports nor play video games.
This research also only investigated the relationships between betting and spectat-
ing esports when defined at the level of competitive video game play, and not within
individual sub-genres. As such, a fruitful avenue for future study would be the com-
parison of consumption behaviours between different esports genres, such as First-
Person Shooter (FPS) or Multiplayer Online Battle Area (MOBA) games. Considering
the distinct structural characteristics of the games, the former has a much shorter and
quicker rounds that the latter, there may be different betting behaviours associated
This work utilised a version of the MSSC adapted for use in the context of esports
consumption. While all amendments were made in accordance with the stipulations of
the original measure, the predictive power was not as strong as had been anticipated. As
such, it may be that the MSSC is not the optimal measure for assessing motivations
underlying the consumption of esports.
This study examined how the consumption of video games, esports and gambling are
associated with esports betting. The results demonstrate associations between spectat-
ing esports and betting on esports, a pattern also observed with respect to participation
20 new media & society 00(0)
in more established gambling activities. Contrary to the stated hypotheses, no direct
association was observed between the consumption of video games and betting on
esports. It may be that video games act as a mediator, as there cannot be esports with-
out video games, yet there is no intrinsic aspect of game play that was associated with
gambling behaviours. This finding builds on an existing body of research that ques-
tions such relationships in contemporary digital culture. However, the associations
between spectating esports, participating in gambling and participation in esports bet-
ting mirror gambling behaviour in traditional sports betting. Although causality cannot
be established, such findings serve to highlight the growing convergence of video
gaming and gambling in digital media as a result of games and gaming culture being
incorporated into novel contexts.
Finally, adapting the MSSC for use in the context of esports revealed that there is a
potential need to develop a dedicated measure for assessing motivations for consuming
esports. Such a measure is likely to provide a valuable contribution to theoretical discus-
sions surrounding distinctions between traditional sports content and that of esports.
The author(s) disclosed receipt of the following financial support for the research, authorship and/
or publication of this article: Dr. Abarbanel has received funding in 2019 from The State of
California Office of Problem Gambling, GLG Consulting, MGM Resorts International, and
ProPress Germany. Dr. Abarbanel has received reimbursement for travel in 2019 from University
of Salford and National Collegiate Athletic Association (USA). During part of 2019, Dr. Abarbanel
was a member of the Singapore National Council on Problem Gambling International Advisory
Panel, for which Dr. Abarbanel was reimbursed for her time. In addition, this research was sup-
ported by a Grant from The Finnish Foundation for Alcohol Studies.
Joseph Macey https://orcid.org/0000-0002-9770-739X
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Joseph Macey is a doctoral researcher at the Faculty of Communication Sciences, Tampere
University, his current work investigates the relationships between the consumption of contempo-
rary digital games and participation in newly-emergent gambling activities. Associated research
interests include problematic and potentially problematic media consumption, cognitive biases in
media users, digital economies and virtual items.
Brett Abarbanel, PhD, is director of research at the UNLV International Gaming Institute, with a
joint appointment as the Head of Social and Recreational Gambling at the UCLA Gambling
Studies Program. Dr. Abarbanel has expertise in global gambling and social science applications,
and her research covers Internet gambling policy and behavior, eSports and gambling, operations
and technology use, and responsible gambling and community relations.
Juho Hamari is a professor of gamification and leads the Gamification Group across Tampere
University and University of Turku. Dr. Hamari’s research group covers several forms of informa-
tion technologies such as games and gamification, new media and online economies. Dr. Hamari
has authored several seminal empirical, theoretical and meta-analytical scholarly articles on these
topics from perspective of consumer behavior, human-computer interaction, game studies and
information systems science which have been published in a variety of prestigious venues.