How Sponsored Content Affects Viewership on Twitch.tv - Masaryk University Faculty of Informatics
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Masaryk University
Faculty of Informatics
How Sponsored Content
Affects Viewership on Twitch.tv
Bachelor’s Thesis
Martin Řehořík
Brno, fall 2017Declaration
Hereby I declare, that this paper is my original authorial work, which I have
worked out by my own. All sources, references, and literature used or excerpted
during elaboration of this work are properly cited and listed in complete
reference to the due source.
Martin Řehořík
Thesis supervisor: prof. PhDr. David Šmahel, Ph.D.
~2~Acknowledgement
Mainly I would like to thank my supervisor prof. PhDr. David Šmahel, Ph.D. for
all the help, guidance, and all the consultations he provided me with. I would
also like to thank all my respondents for taking their time in completing my
survey, if it were not for them, I would not be able to make a relevant case for
my study. Last but not least, I would like to thank my partner Lucie, for being
there for me in times of need.
~3~Abstract
This explorative research is divided into two parts. First part involves testing the
validity of Cheung and Huang scheme of 9 personas that watch eSports events
(i.e. Starcraft II tournaments). Second part aims to find the sponsorship factors
that are related to positive perception of games. The main goal is to find a way
for developers to advertise their game that is most positively perceived. All of
the research is based on the community of variety broadcasters on a live
streaming video platform Twitch.tv.
~4~Keywords
Twitch, eSports, streamer, streaming, channel, viewers, subscribe, persona,
content, sponsorship, sponsored content, active-in-chat developer, factor, survey
~5~Table of contents
Declaration ___________________________________________________________ 2
Acknowledgement _____________________________________________________ 3
Abstract ______________________________________________________________ 4
Keywords ____________________________________________________________ 5
List of tables __________________________________________________________ 8
List of figures _________________________________________________________ 9
Introduction _________________________________________________________ 10
Structure __________________________________________________________ 11
State of the art _______________________________________________________ 12
Basics: Twitch 101 __________________________________________________ 12
Twitch as a third place in society _____________________________________ 14
Alone together in online games ______________________________________ 15
Cheung and Huang's scheme of personas _____________________________ 15
Magic Circle _______________________________________________________ 18
Sponsorship and advertisement______________________________________ 19
Aims and research questions ___________________________________________ 22
Method______________________________________________________________ 23
Sample characteristics ______________________________________________ 23
Measures __________________________________________________________ 25
Analysis_____________________________________________________________ 26
Testing the personas ________________________________________________ 26
Perception of game promotions ______________________________________ 28
Influences on sponsored content _____________________________________ 30
Influences on active-in-chat developers _______________________________ 32
Discussion ___________________________________________________________ 35
Recommendations for developers _______________________________________ 38
Future study _________________________________________________________ 39
~6~Conclusion ___________________________________________________________40
Literature ____________________________________________________________41
Appendix ____________________________________________________________44
Survey ____________________________________________________________44
Tables _____________________________________________________________47
Figures ____________________________________________________________51
Attachments _______________________________________________________53
~7~List of tables
Table 1: Final iteration of factor analysis resulting in 3 components ________ 28
Table 2: How people perceive sponsored content __________________________ 29
Table 3: How people perceive active-in-chat developers____________________ 30
Table 4: Look at R and R2 values of models (sponsored content) ____________ 31
Table 5: Significance of models (sponsored content) _______________________ 31
Table 6: Parameters of multiple linear regression (sponsored content) _______ 32
Table 7: Look at R and R2 values of models (active-in-chat developer) _______ 33
Table 8: Significance of models (active-in-chat developer)__________________ 33
Table 9: Parameters of multiple linear regression (active-in-chat developer) __ 34
Table 10: First factor analysis result including every variable ______________ 47
Table 11: Second factor analysis result (excluding Q07 (The Bystander)) _____ 47
Table 12: Communalities suggests exclusion of variable Q11 (The Unsatisfied) 48
Table 13: Three components explain 57% of the variance ___________________ 48
Table 14: In third iteration of factor analysis, three components explain 64% of
the variance _________________________________________________________ 49
Table 15: Denying the null hypotheses, variables are dependent on each other 49
Table 16: Pearson correlation between factors Spectator and Gamer shows no
connection ___________________________________________________________ 49
Table 17: Testing the reliability of sponsored content battery _______________ 49
Table 18: Testing the reliability of active-in-chat developer battery _________ 50
Table 19: Correlations between independent variables to check if they are fit for
multiple linear regression ______________________________________________ 50
~8~List of figures
Figure 1: Age histogram ________________________________________________24
Figure 2: Histogram of how sponsored content is perceived _________________51
Figure 3: Histogram of how active-in-chat developers are perceived _________51
Figure 4: Histogram of how people see themselves as a gamer factor _________52
Figure 5: Histogram of how people see themselves as a helper factor _________52
Figure 6: Histogram of how people see themselves as a spectator factor ______53
~9~Introduction
Since 1950s, television (TV) has been a part of our culture and has even
played a major part in “moulding public opinions” [Diggs-Brown, 2012].
Nowadays, in the United States, majority of people have access to a TV in their
homes [Season, 2016]. However, it is being slowly pushed out by its online
counterpart such as Youtube, Netflix, Crunchyroll, or by this thesis' main focus
Twitch. Video game live streaming has been popular since 2010 and with its
average of over half a million of concurrent viewers in the year 2015 1, Twitch is
one of the biggest live streaming forces in the industry. What is more, “Twitch
ranked fourth in peak internet traffic for US” as reported in an infographic
published in the Wall Street Journal [Maiberg, 2014].
Twitch provides an easy way of streaming and watching content. This can
be observed not only from individual people streaming (streamers/broadcasters)
games from the comfort of their bedrooms, but also from big events like The
International2 and Worlds3, the biggest eSports tournaments in games Dota 2 and
League of Legends; Games Done Quick4, which is a speedrunning based event
where people attempt to play through a game as quickly as possible and raise
money for charity; Extra Life5 events, a charity organisation doing a 24-hour
fundraising gaming marathons. Other than the foreground of gaming, Twitch
has introduced categories like music [Newman 2015], creative [Machkovech,
2015], social eating [Crecente, 2016], or IRL [Bisaha, 2017]. Constant grow and the
fact that the content is focused on being live (in real time), presents an intriguing
opportunity to have a research based on the websites community.
This thesis acts as an exploratory research based on the communities of
variety broadcasters on a live streaming video platform Twitch.tv. Data used for
this research were collected using an online survey (full text of survey can be
found in the section Survey). This study is focused on two goals. First one is to
characterize who are the viewers on Twitch, based on Cheung and Huang’s
scheme of personas. Second one is to investigate two kinds of game promotions
that are often used on Twitch – sponsored content and active-in-chat developers.
1 Twitch gives public access to these data on a yearly basis in their retrospective
https://www.twitch.tv/year/2015
2 Official website of The International https://www.dota2.com/international/overview/
3 Official website of Worlds
http://www.lolesports.com/en_US/worlds/world_championship_2017/about
4 Official website of GDQ https://gamesdonequick.com/
5 Official website of Extra Life https://www.extra-life.org/
~ 10 ~Structure
In the first part of the thesis, I will go through the state of the art, focusing
on Cheung and Huang’s study, and provide information on Twitch in general, in
order to better understand the infrastructure and also provide information from
other studies concerning third places in society, magic circle, phenomenon of
feeling alone together. In addition, providing a definition of sponsorship by
different authors.
Second part is aimed at answering research questions (RQ), beginning with
brief introduction of method of research, sample characteristics and measures.
Then giving thorough look in the analysis process of each RQ.
The final part includes discussion, ties to the provided theory and gives
recommendations for developers on how to best promote their game according
to the results (socio-informatic aspect of this work). Furthermore, it gives
possible implications for future studies and concludes.
~ 11 ~State of the art
Basics: Twitch 101
Twitch is a live-streaming video platform focused mainly on gaming and
building communities. As such it offers its users to create a free account, with
which they can choose to stream various content or be regular users; both of
these types can participate in watching streams. It is important to mention that
having an account is not compulsory, but it provides users with benefits like
being recognized as a person, being a part of a community and many more.
Home page of Twitch, with its iconic purple logo, represents an easy way
of browsing channels based on what content is being streamed, how many
people are watching particular streams, what is being currently suggested by
Twitch itself, or possibly by geographical place.
Each user is free to personalise their channel and start streaming, offering
other users a place, which they can join and watch provided content. Main way
of communication is via an IRC, which is an integral part of each channel and
fundamental base on which streamers6 can create their communities, where
viewers communicate not only between themselves but with streamers too. All
users of chat must adhere to rules of Twitch7 and individual channels 8. Viewers
can “Follow” channels they like to receive notifications, when said channel goes
live. The number of followers of each channel is public information and is
publicly displayed; however, having a great number of followers does not equal
to great success.
Streamers can apply for partnership in order to gain a status of a partner. To
have their application accepted they must achieve certain goals or rather
guidelines, which are not strictly given. These goals could include things like an
average number of concurrent viewers, certain number of followers, stream for
certain amount of days a week, etc. Partners get to sign a contract with Twitch
after which they gain a “Subscribe” button on their channel – viewers can use
this button to subscribe to a streamer paying a monthly fee varying in price –
4,99 USD, 9,99 USD or 24,99 USD (this fee can later be cancelled at any time).
6 Users who decide to stream.
7 Official Rules of Conduct could be found here https://www.twitch.tv/p/legal/community-
guidelines.
8 As an example, here are general rules of a streamer named CohhCarnage on his official
community website https://www.cohhilition.com/threads/the-big-cohhilition-information-
thread.10445/
~ 12 ~This fee is then divided between streamer and Twitch in accordance to a sum in
their contract. Subscribing to a streamer means that a viewer decided to support
a streamer in a monetized way, rather than participating in a community. Being a
subscriber grants viewers certain benefits, which are unique to all channels
(channel specific emoticons, access to streamer’s personal Snapchat/Instagram
accounts, etc.)
Subscribing to a channel is not the only mean to financially support a
streamer – for example user can donate via a donation system, or third-party
sites like Patreon9. These other means are not, of course, mandatory and the
decision about using them is made purely by streamers themselves, although
majority of streamers do use them to diversify their income (even the ones who
are not partnered).
There are many reasons why are people drawn to be users of Twitch and it
would be impossible to list them all, but Reeves et. al. present an intriguing case
explaining how spectators experience performer’s (streamer’s) action with the
computer and how to properly design the experience for spectators
[Reeves, Benford, O'Malley, Fraser, 2005]. They suggest that spectator’s
experience consist of four main factors, which will be accordingly connected
with Twitch’s scheme – amongst those being the performer’s notion of spectators
and how they can interact and influence the performance. Viewers have a
constant opportunity to interact with a streamer, may it be via messages in chat,
donations or by subscribing to them in untimely moments. Further developing
on this is the sheer presence of an audience, that might make the streamer
uncomfortable in some way [Reeves, Benford, O'Malley, Fraser, 2005].
Two remaining factors consist of how performers manage to set up their
own performance – transition/handover and orchestration. Some might enjoy the
process of transition between performer and a spectator. This happens a lot
during streams in general, when a viewer subscribes or donates to a streamer,
they in most cases, take time to say thank you. Additionally, some streamers
have certain routines on how they do it10. Lastly, orchestration consist of stream’s
9Official website of Patreon https://www.patreon.com/
10Streamer named Ezekiel_III has his own ritual of welcoming someone who subscribes with his
Viking themed channel. Firstly, he puts a helmet on his head, then he positions his fist in front of
the camera as he shouts to welcome a new viewer into his community. Other streamers prefer
different practices, for example some have a whiteboard behind their backs so that they can
write names of each new subscriber during that day.
~ 13 ~direction behind the scenes as a streamer might have prepared different graphic
overlays to change his scene 11.
Twitch as a third place in society
Third place is a concept which Ray Oldenburg established in his book The
Great Good Place [Oldenburg, 1997]. He classifies places into three categories
with different purposes – first being a place where person is at home, second
being a place of work and third being a place set in public meant for socializing.
Third places are fuelled by communication, as in conversation, because that is the
way how people generally socialize. Oldenburg himself sees third places as
“public places that host the regular, voluntary, informal, and happily anticipated
gatherings of individuals beyond the realms of homes and work” [Oldenburg, 1997].
This was further developed and connected with concept of virtual
communities introduced by Howard Rheingold. He suggests that people do not
specifically focus on physical appearances of one another; that gender or race
does not mean as much as in real life face-to-face communication. “People whose
physical handicaps make it difficult to for new friendship find that virtual communities
treat them as they always wanted to be treated – as thinkers and transmitters of ideas
and feeling beings…” [Rheingold, 1993].
Hamilton et. al. in their study, focused on how “live streaming fosters
participation and community” [Hamilton, Garretson, Kerne, 2014], present
interesting findings in regards of stream communities. Abundant number of
people’s first impulse to watch a live stream is to learn about the game. Perhaps,
they just started playing and their main desire is to improve. Other people
watch (or decide to stay watching) because they crave interaction and Twitch is a
suitable place for that. Oldenburg recognizes that “at the core of every third place
are regulars, those people who most frequently visit the place” [Oldenburg, 1997],
which is further acknowledged by Hamilton et. al. saying that regulars are a
strong part of every stream. Regulars can then become moderators, people who
watch over the channel, enforce rules in chat, give useful information about
stream/streamer in general and overall play a big part in socializations of
viewers. Hamilton et. al. also finds that “most moderators are given the status
largely to demarcate them as regulars” [Hamilton, Garretson, Kerne, 2014].
Resulting in a complex construction of a community – streamers set the
11 CohhCarnage often has an entirely new graphic overlay when he plays through a game
franchise.
~ 14 ~foundations that they want their channels to be built on and then together with
the help of regulars build a community.
As we can clearly see, Twitch is a representation of a virtual third place, as
its main drive is communication and interaction. Even when a stream has more
than 10 thousand viewers, streamers still make regards to viewers and
remember certain individuals, inviting others to join them.
Alone together in online games
Ducheneaut et. al. introduced a rather interesting phenomenon that could
support Oldenburg’s view of third places, named alone together [Ducheneaut, Yee,
Nickell, Moore 2006]. This phenomenon is described on a popular MMORPG 12
called World of Warcraft. It is described as a feeling of acknowledgement, that a
massive number of players is playing the same game at the same time, but
people are playing alone, for most of the time, therefore the name – alone
together. Ducheneaut et. al. believes that people “prefer playing a MMORPG to
playing a comparable single-player game because of a different kind of social factor.”
[Ducheneaut, Yee, Nickell, Moore 2006] Their representation of the social factor
includes these three factors – an audience, a sense of social presence, and a spectacle
[Ducheneaut, Yee, Nickell, Moore 2006].
Not everyone feels like being an active part of a community, in fact some
may even resent it, but they still enjoy watching streamers. These people are
called lurkers13. They could also represent someone who is working at that
moment and simultaneously has an open tab in his browser with a Twitch
stream on it. All viewers can use chat whenever they see fit, they are not bound
to socialize, they are aware of these social factors, therefore they can choose
freely to enjoy the feeling of being alone together.
Cheung and Huang's scheme of personas
One of the research questions of Cheung and Huang's research was “Who
are the spectators and why do they spectate?” [Cheung, Huang, 2011] Their research
is based on a popular game franchise Starcraft from Blizzard Entertainment,
because it is one of the cornerstones of eSports gaming in general, as such it is
highly focused on spectators – it could be said that “Like sports, competitive
gaming cannot survive without spectators.” [Su, 2010] Collecting data from various
12 Massive multiplayer online role-playing game
13 People who do not participate in communication in chat.
~ 15 ~origins (text based comments forums and video) of people sharing their stories
about spectating, they managed to identify nine different personas, to help them
define who is each spectator as a person. Personas do not contradict themselves,
in fact someone could be a manifestation of several personas [Cheung, Huang,
2011].
Amongst the nine personas we can find, The Bystander represents
individuals that are outsiders. They are either uninformed or uninvested.
Uninformed are the ones who are not familiar with games. They do not
understand games' fundamental mechanics and the games themselves are
incomprehensible to understand. Uninvested, on the other hand, has some
experience with games, but has not been invested in the game for a while (i.e.
has not been playing). They are flagged as uninvested in the moment they
“stumble” upon a video, or a live event. In reality it could represent someone
(not knowledgeable of games) opening a link, that was sent by a friend, because
it was funny.
The Curious means someone who is in the thrill of acquiring knowledge
about the game, although they are not concentrated on improving themselves
with the game knowledge. As long as there is some uncharted part of the game
that they can learn about, they are elated. Being curious can be a situational
element, because when games are officially released, they can be further
expanded by additional patches (addition to games which add some type of a
new content). This means that curious persona is rather non-linear and that
people could characterize themselves with it based on development cycles of
games.
The Inspired implies that watching is the catalyst for wanting to play a
game. Inspired people either want to play, because they try to simulate what
they saw or play just for the pure joy of playing. Either way they want to feel the
same things as they experienced at the moment of watching.
The Pupil is someone closely related to a curious person. As them, they
want to understand the techniques and gameplay variability, but they take it a
step further. Pupils want to transfer their knowledge about the game into their
actual gameplay, focusing on improving themselves. They want to consume
content which is as detailed as possible, which means watching someone who is
highly competitive, perhaps even a pro-gamer.
The Unsatisfied represents people who would rather be playing a game than
spectating. They substitute being a player into being a viewer, because they are
~ 16 ~not able to play themselves at that moment, because of uncertain reasons – it
could be a technical, social or some other type of a problem for them.
The Entertained values watching rather than playing. This could represent
people who do not have to certainly be players themselves, they just find
excitement in watching. This familiar behaviour could be seen with people
watching sports – someone can watch a football match despite not having a
desire to play an actual game themselves.
The Assistant is someone who are somehow trying to help the person
actively playing the game (in this case – the streamer). It could be seen as an
internal help, focused on gameplay related things (be a second pair of eyes), or
external help, focused on outside of the game related things (snacks, drinks). In
case of this thesis, the external help is not useful, it is even impossible, so only
internal help is acknowledged. Often people who assist streamers with internal
help are called out for being backseat gamers14, which is somewhat of a negative
connotation.
The Commentator has basically the same function as a sports commentator,
they are one of the fundamental parts of eSports gaming. In the same way as
sports commentators they focus on real-time commenting of matches, post-
match analysis and overall overview of their field of interest. As previously with
external help of the assistant, it is not suitable for our community of Twitch. Based
on Hamilton et. al. [Hamilton, Garretson, Kerne, 2014] view on regulars the
commentator persona, in this thesis, is viewed as such. Regulars and moderators
are frequently associated with stream communities and they also represent a
helping hand, therefore they are considered as commentators.
Last of the personas, The Crowd, symbolizes people on the stands. People
find pleasure in feeling like a community and watching a game as a group.
When something significant happens during the game, the crowd reacts
accordingly to it – by showing their emotions (cheering, booing, etc.) Same
situations occur in Twitch’s chat with special emotes like PogChamp15. It is about
being accepted as a part of a crowd.
All personas were defined according to their descriptions in Cheung and
Huang’s study Starcraft from the Stands: Understanding the Game Spectator
14 People who spoil (even in good faith) the active player by telling him how to progress in game
or how the story itself progresses and/or concludes.
15 PogChamp emote is used in situations when something exciting just happened and people are
left with their mouth opened. Other famous emotes include such like Kappa, BibleThump,
DansGame or 4Head.
~ 17 ~[Cheung, Huang, 2011]. It is also described how each persona relates to
community of Twitch viewers and how can it be seen in it. It is important to
describe and understand these definitions because later, during analysis, I will
present questions used in a survey, that represent each persona.
Magic Circle
Magic circle is an “idea of a special place in time and space created by a game.”
[Salen, Zimmerman, 2004] It is a frame with which players can interact and be a
part of the game themselves. By being a part of this game, players take the
responsibility of adhering to the rules of the game. Although the magic circle
represents the interaction between players and the game, we can safely assume
that in a setting such as Twitch, the viewers take their position, either inside or
outside of magic circle. Determining in which position the viewers are, will help
with the conclusion of this thesis.
This is further reinforced by how Salen and Zimmerman see games as
either an open or closed system. They introduce a scheme of game systems, in
which games can be framed as rules, play or culture. [Salen, Zimmerman, 2004].
Each one provides a different view upon a game – this thesis frames a game as a
play, because from the streamers perspective play counts as a closed system, as
they are forced to obey the rules of the game and are the main interactive force
between the game and a player (streamer represents a player). On the other
hand, play also counts as an open system, because viewers on Twitch are
fundamentally viewed as a main interactive component of the site. They can
influence streamers’ opinions, decisions and they understand the rules that
streamers have to adhere to, even though the viewers are not forced to follow
them themselves. Rules set upon viewers are different, since they mostly
comprise of various moral rules (no racism, no sexism, no homophobic
comments, etc.)
In Understanding Video Games: The Essential Introduction, gaming
communities are also a part of this frame. “Studies focusing on the players usually
wish to explore how players use games as a type of medium or as a social space.”
[Egenfeldt-Nielsen, Simon, Tosca, 2015]. It has also introduced two new frames –
ontology and metrics. Each analysis can be a combination of any of these 5 types
of frames; therefore, this thesis is comprised not only of the play, but also of the
metrics type of analysis, focused on game design, mainly helping developers
improve player experience. [Egenfeldt-Nielsen, Simon, Tosca, 2015].
~ 18 ~Furthermore, Suits’s concept of the lussory attitude describes that the
players “adopt rules which require one to employ worse rather than better means for
reaching an end” [Suits, 1978]. One could argue that a better strategy for viewers
to “consume” a game is to play it themselves in order to get the best experience
from it and enjoy mechanics in the game. This could be connected to face-to-face
social interactions, that Peter Berger and Thomas Luckmann described in their
The Social Construction of Reality as “The most important experience of others takes
place in the face-to-face situations.” [Berger, Luckmann, 1966] If we see player-
game interaction (inside the magic circle) as a face-to-face situation, we can
counter-argue that viewers use the concept of lussory attitude by watching a
streamer play the game instead of them. Therefore, as we assumed earlier, we
know that the viewers are indeed a part of the magic circle, one thing left to find
is, whether they are outside or inside.
Sponsorship and advertisement
Drucker et. al. say that “With enough audience, there will be plenty of
opportunities for advertisement, merchandizing, and cross promotion.” [Drucker, He,
Cohen, Wong, Gupta, 2002] Firstly, we must understand how is sponsorship
seen more generally and then we proceed to how we can relate it to Twitch’s
infrastructure.
Sponsorship is a phenomenon that has been greatly compared with
advertising. Definition of it is not a thing that many authors agree on; works of
different authors will be explained in order to get the right idea about
sponsorship. One of the definitions of sponsorship is that it “can be regarded as
the provision of assistance either financial or in-kind to an activity by a commercial
organization for the purpose of achieving commercial objectives” [Meenaghan, 1983].
Cornwell et. al., based on previous literature, proposed a definition of
sponsorship that includes two main activities that are both necessary in order to
have a meaningful promotion. First activity is an exchange between sponsor and
sponsee, in which sponsee receives a fee (amount of the fee has been agreed on
between them) and sponsor receives the right the associate with the activity.
Second activity involves sponsor actively promoting said association in order to
get the knowledge of sponsorship into the world [Cornwell, Maignan, 1998].
Tony Meenaghan brought light into discussion about sponsorship and
advertising in his study Sponsorship and Advertising: A Comparison of Consumer
Perceptions, in which he tried to understand the perception of sponsorship and
adequately compare it to consumer perception of advertising [Meenaghan,
~ 19 ~2001]. The goodwill generation is brought to mind as a main difference between
sponsorship and advertising, because it is related to the investment of a sponsor
in a certain type of capital that the consumer has a substantial emotional
involvement with. The belief was that goodwill directly benefits the thing being
sponsored16, therefore it is not all about self-promotion and self-gain. That is
why sponsorship was more favourable than advertising. [Meenaghan, 2001].
Common sense says that goodwill aimed at charity is naturally rewarded with a
greater positivity. Advertisement is seen as a negative opposite, because it is
seen as rather selfish form of promotion. Degree of directness and subtleness
also plays a role in the difference – sponsorship is in its basis indirect 17, someone
could fail to notice that some event is sponsored, on the other hand
advertisement is as direct as it can be [Meenaghan 2001]. Clear distinction comes
to mind between sponsorship and advertisement. Adverts are mainly focused
on promoting certain products, sponsorship is believed to focus on brand
[Meenaghan, 2001].
However favourable the sponsorship is, Meenaghan clearly stated that “it
should not be assumed that sponsorship as a consequence is more effective than
advertising in terms of generating specific desire consumer responses” [Meenaghan
2001]. What is more, it is not generally agreed that sponsorship influences
purchase behaviour [Hansen, Scotwin, 1995].
Certainly, sponsorship represents some negative facets that could be not
favoured by people. Amongst those are sponsor interference, fear that sponsor
could somehow manipulate the fundamentals of the sponsored thing and make
it less desirable to watch for consumers. Even though the goodwill factor is
something that makes people positively perceive sponsorship, it is not always
regarded because sponsors might be focused on high-profile targets. Another
facet is ticket allocation. People might be afraid that sponsors get higher valued
“tickets” automatically, preventing actual fans from buying them. Finally, degree
of exploitation is the biggest negative facet, it is closely tied to sponsor interference.
Sponsors might demand treatment that is highly unwanted by people, because
it gets too much in their way of enjoyment [Meenaghan 2001].
Congruence is one of the factors that consumers use to judge how
sponsorship fits certain events; they assess it in two different ways. Firstly, how
the product itself connects with the activity being sponsored is assessed.
16 Cornwell et. al. evaluated it in the same way “Indeed, by sponsoring specific activities, a business
can signal to its customers that it shares their interests or supports their favourite causes.” [Cornwell,
Maignan, 1998]
17 It was also described as a “mute, non-verbal medium” [Meenaghan, 1983]
~ 20 ~Someone might understand the connection between a beverage sponsor at a
football event and others might see that there is no link between these things.
Secondly, how the consumers themselves perceive the sponsor as a brand.
Someone enjoys a particular thing and therefore see a positive congruence; the
rest might prefer a different brand, which leads to them having a negative
congruence. It is important for sponsors themselves to judge how their product
fits with the sponsored audience [Koo, Quarterman, Flynn, 2006]. Relevancy of
sponsorship was also studied by Hansen et. al. They found that relevant
sponsors were more remembered, more liked and influenced higher purchase
intentions [Hansen, Scotwin, 1995].
Authors collectively suggest many different strategies on how to use
sponsorship most advantageously and who should they sponsor. Beck said that
“Sponsors favour the performing over the visual arts; the known and the familiar over
the novel; prestigious, well-established ‘big names’ amongst theaters, orchestras and
galleries; and London over everywhere else.” [Beck, 1990]. Successful sponsorship
should empower sponsor’s brand equity [Cornwell, Maignan, 1998]. It has been
also suggested that sponsorship should not act alone and should be in
combination with other instruments, because it greatly increases its impact
[Walliser, 2003].
~ 21 ~Aims and research questions
The aims of this thesis are divided into two parts. Research questions are
derived from this thesis’s theory. First part includes testing Cheung and
Huang’s theory on personas. RQ1 is concerned with the scheme of personas
[Cheung, Huang, 2011]. The scheme will be properly tested on Twitch.tv and
possibly adjusted if it does not explain the communities of variety streamers on
Twitch.
RQ1: Is Cheung and Huang scheme of personas valid in the
community of variety streamers?
Second part includes finding the best way to promote a game on
Twitch. Two kinds of game promotion are represented in this thesis. The
first kind is sponsored content, which is seen as a traditional way of game
promotion (sponsorship, which was thoroughly described in the section
Sponsorship and advertisement). When streamers are sponsored, they are
required to play a certain game for a certain amount of time and of course
obey their contract18. Active-in-chat developer, the second kind of game
promotion, is a term I use specifically for this thesis. It is meant to
represent more of a non-standardized way of game promotion – more
active rather than passive. In this case, the developer is actively
participating in the communication in chat (this form of communication
might include chatting, giveaways, generally being friendly, replying to
answers about their game, etc.) It is a hands-on contact with the streamer
and the community that can be seen publicly by everyone who is currently
watching. It can be considered as a new way of “sponsoring” games on
Twitch. These two promotions will be compared which one is more
positively perceived. Then it will be investigated via a thorough analysis
which variables influence people in their perception. This part is aimed at
answering these research questions:
RQ2: How are certain kinds of game promotions perceived?
18Contracts might even say to not speak ill of the game, but this depends on certain contracts
and on streamers themselves if they want to remain their integrity. Each contract is different, so
the precise number of hours the streamer must stream the game, etc. is not known.
~ 22 ~RQ3: How are demographics and persona factors related to
perception of sponsored streams?
RQ4: How are demographics and persona factors related to
perception of active-in-chat developers?
Method
This thesis is based on being an exploratory research into the communities
of variety streamers on Twitch.tv. Data used in this survey were gathered using a
quantitative online survey; software for data collection was Google Forms and the
survey was open to public in the course of three weeks – from 18th September to
9th October. Completion of the survey took approximately 10 minutes. Survey
has been widely distributed into communities of different streamers19, where it
was periodically sent out. Communities gather at various places, therefore a
wide selection of websites/forums/chats was selected. Amongst those places
were Twitch IRC’s, Discord servers, Reddit forums and community websites of
certain streamers. In some cases, to gain access to IRC or Discord I had to
personally subscribe to streamers, because they were open only for subscribers.
The biggest surge of responses was from Reddit; admins of subreddit Twitch
allowed me to share my survey there, although it is normally again the rules to
share a survey. Everyone completed the survey willingly with no motivation to
win something. To counteract the possible personal bias, deep connection
between related work and personal experience has been made. Data were
analysed using the software IBM SPSS Statistics 23.
Sample characteristics
Sample originally consisted of 818 respondents. Each respondent
completed the survey fully, because everything was compulsory to fill out.
However, certain data exclusion had to be done, mainly because of outliers and
some fake responses, this included 58 respondents. The final size of sample is
760 respondents aged 14–49 years with 91% (N = 688) males. Family status
reported that 70,4% (N = 535) of the sample is “Single”; 20,4% (N = 155) is “In a
relationship”; and only 8,4% (N = 64) is “Married”. Overall level of highest
achieved education is solid with 46,6% (N = 354) having either elementary
19 Those streamers being: DansGaming, GiantWaffle, LIRIK, ItmeJP, CohhCarnage, Ezekiel_III.
~ 23 ~school or secondary school (high school); 43,1% (N = 327) having a bachelor’s
degree; and 10,3% (N = 78) achieving master’s or doctor’s degree.
Figure 1: Age histogram
Resulting sample is not perfectly normal (Figure 1), but is suitable for the
purpose of this study. Sample is consisted of 89% Millennials20, the rest is Gen
X21. Majority of the respondents (36%) is from the United States, other significant
countries include United Kingdom (10%), Germany (8%), Canada (6%), Sweden
(6%). These results could be compared to previous study by Kaytoue et. al. in
which they found that highest activity on Twitch comes from United States
[Kaytoue, Silva, Cerf, Meira, Raissi 2012].
20 People born between 1982-2000.
21 People born between 1961-1981.
~ 24 ~Measures
These predictors are used for answering all research questions.
Personas. Battery of nine question was created to measure how many
people see themselves as certain personas. It was created with Cheung and
Huang scheme of personas in mind and also with help of related work
[Hamilton, Garretson, Kerne 2014]. Each persona has exactly one question.
Responses to these questions are 4-point Likert scale with no neutral option,
where 1 = Certainly yes and 4 = Certainly no [Allen, Seaman, 2007]. The only
exception to this is Q07, where a fifth option was added to make the whole
battery coherent. This battery is used for factor analysis to define certain factors
of who people are on Twitch. These factors are then accordingly scaled to one
variable each (using procedure Mean) for further use.
Perception of sponsored content and perception of active-in-chat
developers. Additional batteries consisted of 7 questions, each (Q16_a – Q16_g
and Q17_a – Q17_g) is used to determine perception of game promotions. As a
regular viewer, I was able to recognize certain behavioural patterns, which have
been greatly expressed via chat, therefore these questions were formulated
using my know-how of Twitch. Same Likert scale is used for these batteries.
Using procedure Mean, both batteries were computed into two different scales
(sponsored content = 0,77 Table 17, active-in-chat developer = 0,68 Table 18)
Socio-demographic predictors are thoroughly described in Sample
characteristics.
Twitch related predictors. Some characteristic information on respondents
regarding Twitch had to be answered. Details on how many hours respondents
watch Twitch (Mean = 22, SD = 16) and how many streamers they watch (Mean =
7,7, SD = 7,7), were collected using open-ended questions asking for an integer
value. Information on how often the respondents are on Twitch had nominal
measure where 1 = Daily and 6 = Once every few months (80% people responded
Daily). Preconception for this question is that respondents have used Twitch in
last year, therefore option Never was excluded).
Gaming related predictors. How respondents interact with games is also a
valuable information. Thus, respondents reported how many hours weekly they
play videogames (Mean = 21,9, SD = 15,9), how much variety is in their gaming
on a scale 1-10 (Mean = 6,4, SD = 2,3), where 1 = Non-variety (one game genre) and
10 = Variety (every genre of games). Finally, how often they buy videogames with
~ 25 ~nominal measure, where 1 = Daily and 7 = Never (40% reported Once every few
months, 26% Couple times a month and 19% Once a month).
Analysis
Testing the personas
First thing that had to be done was to properly transform Cheung and
Huang’s personas [Cheung, Huang, 2011] into survey questions. Definitions
were accordingly transcribed so that every persona fits on the platform of
Twitch. Amongst some of the biggest changes are: The Bystander persona is no
longer divided into uninformed/uninvested rather it is merged into one; the
Entertained focuses on the entertainment that streamer provides; the Assistant
represents a backseat gamer; the Commentator represents regulars and/or
moderators; and the Crowd focuses on the feeling of the audience. Here are the
formulated questions:
Q07 (The Bystander) – Do you watch streams even though you are not
a video game player?
Q08 (The Curious) – Are you curious to learn something more about
the game while watching the stream?
Q09 (The Inspired) – Does watching a stream make you want to play a
game?
Q10 (The Pupil) – Do you want to learn something to improve your
gameplay while watching the stream?
Q11 (The Unsatisfied) – Would you prefer to play a game instead of
watching someone play it? (i.e. you are not able to play it at the
moment because of various reasons)
Q12 (The Entertained) – Do you watch only because of the
entertainment that the streamer provides? (i.e. you don’t care about
the game being streamed)
Q13 (The Assistant) – Do you often find yourself somehow helping a
streamer? (e.g. telling him solutions to riddles, sharing interesting
facts, …)
~ 26 ~Q14 (The Commentator) – Do you see yourself as some kind of a
moderator (even if you are not) in chat?
Q15 (The Crowd) – Do you feel like you are a part of an audience
while watching the stream?
Going into the first factor analysis, we can see that resulting components
(Table 10) are not satisfactory (even though they are fit for factor analysis),
therefore I decide to leave out variable Q07 (The Bystander). Reason for leaving
this variable out is that 81% of people responded with “I am a gamer.” This does
not represent the purpose of this persona and with only 19% of people, who do
not consider themselves to be gamers, it only misrepresents the data22.
Second iteration of factor analysis (Table 11) leaves us with much better
results than before, but table of communalities (Table 12) suggests leaving out
another variable. In this case it is variable Q11 (The Unsatisfied). Considering that
this result explains only 57% of the variance (Table 13), I conclude that a third
iteration is needed.
Third, and final, iteration leaves the best results so far. Factors collectively
explain 64% of the variance (Table 14) and no other variable is suggested for
exclusion, likewise Keiser-Meyer-Olkin (KMO) indicates satisfying result (Table
15). Matrix of components (Table 1) provides even better results amongst
variables of each factor. Using varimax rotation we get three different
components; next step is naming them.
22 I feel safe about leaving out this variable also because theory suggests that main drive for
people to start watching at first is to learn about the game that is being streamed [Hamilton],
that implies that majority of viewers are viewed as players. In addition, if we look at LifeCourse
study [Wright, 2014], they found that 73% of Millennials and 63% of Gen X have played a game
in the last 60 days.
~ 27 ~Rotated Component Matrix
Component
1 2 3
Q10 (The Pupil) ,803
Q08 (The Curious) ,768
Q09 (The Inspired) ,691
Q14 (The Commentator) ,838
Q13 (The Assistant) ,819
Q12 (The Entertained) ,840
Q15 (The Crowd) ,328 ,613
Table 1: Final iteration of factor analysis resulting in 3 components
First factor is heavily focused on gaming, because it is consisted of three
game related questions, therefore name Gamer is the most suitable one. Gamer is
concentrated on getting more knowledge about the game. It could either be
theoretical, understanding the fundamentals and intricacies of the game, or
practical, learning to be a better player by watching. Either way they feel
inspired to play when they watch.
Second factor is one that is focused on communication the most out of the
three factors, but it is not its entire meaning. Purpose of this factor is to help; to
find social connection through helping. Accordingly, it is named Helper23.
Third factor is the Spectator and this factor represents the interest in
streamer and entertainment. People who belong to this factor acknowledge the
feeling of being a part of an audience and they enjoy watching streams because
of the streamers who give them the feel of entertainment rather than getting it
from the game itself.
Perception of game promotions
This section will examine how the respondents see two game promotion
kinds in question. Not looking at possible influences at the moment, just
focusing on raw perception. Two tables are compared to each other (Table 2 to
23Regular would be also a good name choice for this factor, but to avoid any confusion with
related work I rather chose Helper.
~ 28 ~Table 3); examining each question from the batteries looking for a better result
amongst one of them. Green signalizes a more positive perception than red24.
How do you feel/react when the stream is sponsored (the streamer is paid to play a
certain game)?
Certainly Rather Rather Certainly
yes yes no no
Yes No
1,1 15,2 53,1 30,6
Q16_a: I feel more interested in the game
16,3 % 83,7 %
0,7 11,9 49,5 37,8
Q16_b: I am more inclined in buying the game
12,6 % 87,3 %
3,3 11,2 52,1 33,4
Q16_c: I am more fond of watching the stream
14,5 % 85,5 %
Q16_d: I think that the streamer has a biased opinion 14,1 37,1 34,5 14,3
about the game 51,2 % 48,8 %
Q16_e: I want to leave the stream because it's 5 11,4 35,6 47,9
sponsored 16,4 % 83,5 %
4,3 16,6 44,5 34,6
Q16_f: I feel bored
20,9 % 79,1 %
42,5 35,3 16,3 5,9
Q16_g: Doesn't matter to me, I watch anyways
77,8 % 22,2 %
Table 2: How people perceive sponsored content
High contrast between answers could be seen amongst some of the
questions in the batteries. What does this difference mean will be properly
investigated in Discussion.
24Highlighted results signalize that variable d, e and f will be later recoded so that the batteries
can be scaled (required for further analysis).
~ 29 ~How do you feel/react when a developer is somehow active in chat (chatting, giveaways,
Q&As, being friendly, …)?
Certainly Rather Rather Certainly
yes yes no no
Yes No
31 42,6 16,5 10
Q17_a: I feel more interested in the game
73,6 % 26,5 %
13,1 36,9 33,7 16,3
Q17_b: I am more inclined in buying the game
50 % 50 %
19,2 41,4 26,3 13,1
Q17_c: I am more fond of watching the stream
60,6 % 39,4 %
Q17_d: I think that the streamer has a biased opinion 9,8 29,9 40,2 20,1
about the game 39,7 % 60,3 %
1,6 2,9 33,8 61,7
Q17_e: I want to leave the stream because of it
4,5 % 95,5 %
1,4 3,2 41,4 54,1
Q17_f: I feel bored
4,6 % 95,5 %
42,5 40,2 12,4 4,9
Q17_g: Doesn't matter to me, I watch anyways
82,7 % 17,3 %
Table 3: How people perceive active-in-chat developers
Influences on sponsored content
Why people perceive sponsored content in such a way is a tricky question,
which will be examined closely. Answering RQ3 and RQ4 will require using
factors and scaled batteries of game promotions, therefore a check of normality
of these new variables is required (Figure 2 – Figure 6), check for reliability of
game promotion batteries (Table 17 and Table 18) and finally a check of
correlations between every independent variable going into the further analysis
(Table 19).
Multiple linear regression (method enter) is used to predict how
independent variables influence dependent variable. In this case dependent
variable is represented by sponsored content scale (Figure 2) and independent
variables are those with which correlations were found (Table 19) – that
includes Model 1 – factor Gamer, factor Spectator and Model 2 includes
additional variables – q05 (variety gamers scale), q19 (age).
~ 30 ~Model Summary
Std. Error
Adjusted R
Model R R Square of the
Square
Estimate
1 ,212 ,045 ,043 ,50089
2 ,227 ,051 ,046 ,49988
Table 4: Look at R and R2 values of models (sponsored content)
ANOVA
Model Sum of Squares df Mean Square F Sig.
Regression 8,957 2 4,478 17,850 ,000
1 Residual 189,925 757 ,251
Total 198,882 759
Regression 10,224 4 2,556 10,229 ,000
2 Residual 188,658 755 ,250
Total 198,882 759
Table 5: Significance of models (sponsored content)
Table 4 and Table 5 show that defined models are fit for multiple linear
regression, they neither show huge correlations nor they show perfect precision,
but they are still significant, therefore a thorough further analysis is required.
~ 31 ~Coefficients
Standardized
Unstandardized Coefficients
Model Coefficients t Sig.
B Std. Error Beta
(Constant) 2,036 ,081 25,026 ,000
Factor of
interest in ,093 ,032 ,103 2,897 ,004
games
1
Factor of
interest in
,149 ,029 ,180 5,073 ,000
streamer and
entertainment
(Constant) 2,002 ,122 16,462 ,000
Factor of
interest in ,091 ,032 ,101 2,836 ,005
games
Factor of
interest in
,139 ,030 ,168 4,659 ,000
streamer and
2 entertainment
Age ,005 ,003 ,062 1,734 ,083
On a scale 1-10,
how do you
consider -,011 ,008 -,052 -1,447 ,148
yourself being a
variety gamer?
Table 6: Parameters of multiple linear regression (sponsored content)
Table 6 shows that Model 2 is not fit for our case, therefore only Model 1
including two factors will be used formulation of the equation and conclusion.
Influences on active-in-chat developers
Models for this multiple linear regression (method enter) have to be
slightly adjusted. Dependent variable is represented by active-in-chat developer
scale (Figure 3). Independent is Model 1 which will consist of every factor, that
has been found and Model 2 will consist only of Q19 (age) – everything is
chosen in accordance to Table 19.
~ 32 ~Model Summary
Std.
Adjusted R Error of
Model R R Square
Square the
Estimate
1 ,256 ,066 ,062 ,46068
2 ,268 ,072 ,067 ,45945
Table 7: Look at R and R2 values of models (active-in-chat developer)
ANOVA
Sum of
Model df Mean Square F Sig.
Squares
Regression 11,282 3 3,761 17,720 ,000
1 Residual 160,440 756 ,212
Total 171,722 759
Regression 12,348 4 3,087 14,624 ,000
2 Residual 159,374 755 ,211
Total 171,722 759
Table 8: Significance of models (active-in-chat developer)
Table 7 and Table 8 show more satisfying result than in the previous
chapter. It can be safely said that models are linear and fit for linear regression.
Linear regression (Table 9) approves both models, therefore Model 2 is
chosen to answer RQ4.
~ 33 ~Coefficients
Standardized
Unstandardized Coefficients
Model Coefficients t Sig.
B Std. Error Beta
(Constant) 1,350 ,097 13,848 ,000
Factor of
interest in ,164 ,030 ,196 5,508 ,000
games
Factor of
social
1 connection ,054 ,024 ,082 2,279 ,023
through
helping
Factor of
interest in
,082 ,027 ,107 3,008 ,003
streamer and
entertainment
(Constant) 1,212 ,115 10,547 ,000
Factor of
interest in ,163 ,030 ,194 5,485 ,000
games
Factor of
social
connection ,052 ,023 ,079 2,206 ,028
2
through
helping
Factor of
interest in
,077 ,027 ,100 2,801 ,005
streamer and
entertainment
Age ,006 ,003 ,079 2,248 ,025
Table 9: Parameters of multiple linear regression (active-in-chat developer)
~ 34 ~Discussion
Factor analysis suggests that there are three factors, which are named
appropriately based on their functions. Gamer is a factor of interest in games, as
such it connects to theory that Hamilton et. al suggest [Hamilton, Garretson,
Kerne, 2014]. The main drive for people to watch is to learn about games. People
are drawn to learn about the games on Twitch because of its rawness, because
the content is broadcasted live and not edited. When people become regulars,
they get to know the streamers – their likes, feelings, and they get to understand
their behaviour. It is easier for them to judge how honest the streamers are;
therefore, they can truly learn about the game – learn how it feels to play the
game, which could be an essential element for some, because it is not all about
graphics and gameplay. For example, when people get to know the streamer and
see that they are frustrated, they can properly judge if the game difficulty is
really that hard. That is why some people enjoy learning about games on
stream, because it can feel like a true hands-on contact. If the original Cheung
and Huang’s scheme were to remain intact, persona The Unsatisfied (Q11) would
be a part of this factor (Table 11), because the Unsatisfied cares about playing the
game rather than watching
Hamilton et. al. also talk about the core of every community – regulars and
moderators [Hamilton, Garretson, Kerne, 2014]. Accordingly, the second factor
that came out from factor analysis (Table 14) is the Helper. This factor is
comprised of regulars and moderators (and possibly backseat gamers), because they
naturally care about the community. Their way of involving themselves with the
community is by helping either the streamers or the viewers. Therefore, they
rightfully take place in hearths of every community. They help shape the
streamers’ channel, because they are the ones streamers are most connected with.
Factor Helper also correlates with variable Q15 (The Crowd). That is
understandable, because their communication takes place in the audience itself,
therefore they feel like a part of it – but it is not an essential part of the Helper.
Third factor, Spectator, is special for Twitch, not because other websites do
not create communities such as Twitch. It thrives on being a part of an audience,
like in a theatre. They care about the entertainment and not about the game, in
fact they neither care about learning or improving (Table 16). In the first
iteration of factor analysis The Bystander (Q07) was excluded. This is rather
interesting because now it could be seen, that this persona fits into this factor,
because it represents someone who does not care about the game in the first
place. Phenomenon of alone together by Ducheneaut et. al. also connects with this
~ 35 ~factor. Spectators do not necessarily have to interact in chat but they are a part of
an audience, they feel the social presence and they enjoy the spectacle – all of these
aspects are naturally present on every Twitch stream [Ducheneaut, Yee, Nickell,
Moore, 2006]. Special type of people – lurkers – are also involved in this factor,
representing people who do not necessarily engage in communication but
understand that they are a part of an audience.
Thus, the answer to RQ1 is yes, Cheung and Huang’s scheme of personas
[Cheung, Huang, 2011] is valid on communities of variety streamers on Twitch.tv.
Factor analysis helped form the original nine personas into three factors, because
some personas are closely related to each other. Factors Gamer, Helper, Spectator,
represent Twitch community at large – they consist of everything that is essential
to Twitch and what makes it stand out from other services. Even the personas
that were left out in the process of factor analysis, could be theoretically
connected to certain factors as theory suggests. Definition of these three factors
also grants a chance to see where viewers on Twitch belong in accordance with
magic circle. Gamers are inside the magic circle, because of how connected they
feel to the game; the remaining factors helper and spectator belong outside of the
magic circle, because they are not so greatly focused on the game itself and
fundamentally do not interact with it as much as the gamer.
Answer to RQ2 lies in the comparison of Table 2 with Table 3. Results
show that active-in-chat developers are more positively perceived in every matter
the survey focused on. It is important to notice that each battery is consisted of
three questions focused on positive emotions (a, b, c), three questions focused on
negative emotions (d, e, f) and one is neutral (g). High contrast between positive
emotions of each battery is observable, but on the other hand amongst the
questions concerning negative and neutral emotions, there is no substantial
difference25.
This comparison speaks to how certain kinds of game promotions are
perceived, therefore answering RQ2. Active-in-chat developers are perceived
considerably more positive in every aspect, but that does not imply that
sponsored content is viewed more negatively, in fact it is not. Looking at the
questions of negative emotions, they are perceived in almost the same matter in
each battery. Meenaghan’s negative facets of sponsorship are not influencing the
negative perception of game promotions [Meenaghan, 2001]. That leaves a
rather fascinating result – the only emotions that are incentivized are positive
ones, people do not change their mind about games in a negative way.
25 The biggest one is approximately 16% between Q16_f and Q17_f.
~ 36 ~You can also read
