Why do people watch others play video games? An empirical study on the motivations of Twitch users

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DOI: 10.1016/j.chb.2016.10.019
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Abstract
This study investigates why people choose to watch others play video games, on services such as Twitch. Through a questionnaire study (N = 1097), we examine five distinct types of motivations from the uses and gratifications perspective: cognitive, affective, personal integrative, social integrative and tension release. Information seeking is shown to be positively associated with the amount of hours that users chose to spend on the service, as well as the amount of individual streamers they choose to watch. Furthermore, we find that tension release, social integrative and affective motivations are positively associated with how many hours people watch streams. We also find that social integrative motivations are the primary predictor of subscription behaviour. This study lays the groundwork for understanding the motivations to consume this emerging form of new media in the context of online games and video streams.
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Why do people watch others play video games? An empirical study on
the motivations of Twitch users
Max Sj
oblom
a
,
b
,
*
, Juho Hamari
a
,
c
a
Game Research Lab, School of Information Sciences, University of Tampere, FIN-33014 Tampere, Finland
b
Aalto University School of Science, P.O. Box 11000, FI-00076 Aalto, Finland
c
Tampere University of Technology, Finland
article info
Article history:
Received 11 August 2016
Received in revised form
7 October 2016
Accepted 21 October 2016
Available online xxx
Keywords:
Streaming
Uses and gratications
eSports
Media usage
Let's play
abstract
This study investigates why people choose to watch others play video games, on services such as Twitch.
Through a questionnaire study (N ¼1097), we examine ve distinct types of motivations from the uses
and gratications perspective: cognitive, affective, personal integrative, social integrative and tension
release. Information seeking is shown to be positively associated with the amount of hours that users
chose to spend on the service, as well as the amount of individual streamers they choose to watch.
Furthermore, we nd that tension release, social integrative and affective motivations are positively
associated with how many hours people watch streams. We also nd that social integrative motivations
are the primary predictor of subscription behaviour. This study lays the groundwork for understanding
the motivations to consume this emerging form of new media in the context of online games and video
streams.
©2016 Elsevier Ltd. All rights reserved.
1. Introduction
Hundreds of millions of users choose to spend their time
watching others play video games through live internet broadcasts,
referred to as streams, on services such as Twitch. This type of new
media has both been made possible and fueled by the ever
increasing bandwidth of networks, advances in video packing and
encoding technologies, a user-generated content culture, and, ul-
timately, by the desire to see others play video games. Today, peer-
to-peer internet streaming of video games is a rapidly growing
form of media. Recent years have seen services doubling their user
base year-on-year, with current gures reaching over a hundred
million unique monthly users (Ewalt, 2014; Needleman, 2015;
Twitch, 2015).
Streaming is an extremely interesting context for participatory
online media, spearheaded by services such as YouTube, that have
put the traditional consumer into the role of content creator (Cha,
Kwak, Rodriguez, Ahn, &Moon, 2007). Content creators such as
PewDiePie challenge traditional media corporations, having over
27 million subscribers on YouTube alone in 2014 and over 40
million at the time of writing, showing the impact a single indi-
vidual can have on the media landscape (Grundberg &Hansegard,
2014). One might regard streaming as yet another form of broadcast
entertainment akin to online videos, but for many users it is a more
manifold and holistic communication channel than mere video
media content, particularly due to the high levels of interaction.
Due to the live-broadcasting nature of video game streaming, it
offers a unique relationship between the media creator and media
consumer, thus facilitating communication between the two. Other
forms of new media such as YouTube have already adopted prac-
tices common to social network sites (SNS) (Boyd &Ellison, 2007;
Lange, 2007), however video game streaming services take these
participatory aspects one step further as the interaction is taking
place in real time. Video game streaming also blends two distinct
mediums: broadcast media and games. While television spectating
has largely been considered to be a unidirectional activity, games
are commonly perceived as a multi-directional activity requiring
active user participation. Hence, a mixture of these dominant me-
dia forms leads to an interesting context of spectating video games
with a degree of interaction, thus causing an experience that is
more passive than playing games, but at the same time more active
than consuming traditional television content.
However, it is not fully clear why peer-to-peer internet
*Corresponding author. Game Research Lab, School of Information Sciences,
University of Tampere, FIN-33014 Tampere, Finland.
E-mail addresses: max.sjoblom@uta.(M. Sj
oblom), juho.hamari@uta.
(J. Hamari).
Contents lists available at ScienceDirect
Computers in Human Behavior
journal homepage: www.elsevier.com/locate/comphumbeh
http://dx.doi.org/10.1016/j.chb.2016.10.019
0747-5632/©2016 Elsevier Ltd. All rights reserved.
Computers in Human Behavior xxx (2016) 1e12
Please cite this article in press as: Sj
oblom, M., &Hamari, J., Why do people watch others play video games? An empirical study on the
motivations of Twitch users, Computers in Human Behavior (2016), http://dx.doi.org/10.1016/j.chb.2016.10.019
streaming gathers such large crowds of spectators, and if this
growth is a sign of a more general trend in media consumption and
information seeking, or merely a niche form of entertainment. As
we do not have a clear grasp of the motives driving consumption
behaviour, we see it as paramount to explore these motivations in
order to build a deeper understanding. Therefore, in this paper we
seek to explore and measure why so many people are choosing to
watch others play games over the Internet, focusing specically on
the context of video game streaming which is the largest form of
such online live peer-to-peer media production and consumption.
We employ data gathered through an online survey (N ¼1097) and
analyse the data by employing structural equation modelling.
2. Background
Video games have had a certain social spectating element to
them from their inception. In the early days of arcade games,
people would gather around the person playing the game to see
how they were doing and to cheer them on (Newman, 2004), and,
later, LAN gatherings encouraged face-to-face interaction (Jansz &
Martens, 2005). When games moved from the arcades to living
rooms, players were no longer subject to the stares of strangers
when playing their favourite games. With the emergence of video
game streaming, we argue that a part of this social experience has
been brought back to the gaming culture. While video games have
been the subject of decade's worth of studies within multiple elds
of research (for a recent overview see Quandt et al., 2015), real-time
video game streaming is a novel development that has potential to
contribute to the growing area of games research within the in-
formation sciences, media and communication domain.
Streaming typically refers to conveying media content in a way
that it is simultaneously consumed by the receiver, as opposed to
downloadingwhere the received media content is saved for later
consumption. Hence the term streaming is more concerned with
the delivery method of the medium rather than the exact form of
the medium (Gelman, Haln, &Willinger,1991). Internet streaming
has existed for a long time and in various forms. However, it is
important to distinguish between streaming as a technological
solution, and the cultural phenomenon of live broadcasts of user-
generated content, also commonly referred to as streaming.
2.1. Characteristics of video game streaming
In previous media research, media have been classied on the
basis of their delity and participatory nature. Media types
requiring a higher degree of participation have been coined as cool,
while types of media where the information is presented in more
abundance and requiring less participation have been called hot
(McLuhan, 1994). In research centred on video game streaming,
mixed types of media have been shown to be of importance for
viewers (Hamilton, Garretson, &Kerne, 2014). The mix of media
allows for a highly interactive experience, with the hot media
(video content) serving as a facilitator for interaction via the cool
media (chat functionalities). The popularity of streamed game titles
has been shown to vary greatly over time (Kaytoue, Silva, Cerf,
Meira, &Raïssi, 2012). While the most popular games continue to
be established eSports, the release of new game titles partially ac-
counts for large uctuations in the viewer distribution. Addition-
ally, stream popularity was shown to follow a highly skewed Pareto
principle, with 10% of individual streamers accounting for 95% of
viewers (Kaytoue et al., 2012).
2.2. Uses and gratications
The question of why people consume different forms of media is
one of the main areas of inquiry in communication and media
sciences. In this vein of research, the most prevalent theoretical
development and framework is perhaps the Uses and Gratication
(UG) theoretical perspective (Katz, Blumler, &Gurevitch, 1973;
Katz, Gurevitch, &Haas, 1973; Rubin, 2002; Ruggiero, 2000).
Contrary to previous media theories such as mass society the-
ory, UG considers media to have onlya limited effect. UG states that
the motivation behind using a certain media is a particular grati-
cation that is sought (Katz, Blumler, &Gurevitch, 1974; Ruggiero,
2000), and posits that the user seeks out their media of choice, as
an active audience, rather than the media seeking out the user
(Abercrombie &Longhurst, 2007; Baran &Davis, 2006; Wang, Fink,
&Cai, 2008). Furthermore, UG states that the media competes for
gratication with other sources than only those related to media
(Katz, Blumler et al., 1973). Large individual differences can exist, as
UG considers users as individuals rather than a larger mass (Katz
et al., 1974). Within UG, needs are often classied in ve cate-
gories (Cognitive, Affective, Personal Integrative, Social Integrative
and Tension Release), as presented in Table 1 (Katz, Gurevitch, at al.,
1973; West &Turner, 2010).
UG considers media users to be aware of their own usage, and
therefore able to provide researchers with an accurate idea of their
media consumption habits and motivation (West &Turner, 2010).
UG has been used in a wide range of different communication
research contexts, such as television (Krcmar &Greene, 1999;
Schmitt, Woolf, &Anderson, 2003), personal communication
(Ishii, 2006), and also in the notion of multitasking (Wang &
Tchernev, 2012). Another area where UG has been heavily used is
the online context (Ko, Cho, &Roberts, 2005; LaRose &Eastin,
2004; Papacharissi &Mendelson, 2010; Whiting &Williams,
2013), including online games (Sherry, Lucas, Greenberg, &
Lachlan, 2006; Wu, Wang, &Tsai, 2010), Facebook (Joinson,
2008), video streaming (Cha, 2014; Chiang &Hsiao, 2014) and
Twitter (Chen, 2011; Johnson &Yang, 2009). In this study, we aim to
look at the motives for watching video game streams through the
lens of UG.
The nature of video game streaming as a new and yet unex-
plored medium makes it an interesting target of investigation from
the UG perspective, because it enables comparisons to be drawn
between it and other, more traditional, forms of media. Watching
others play is a highly alluring subject for study, especially since it
remains rather unintuitive for many people as to why watching
video games would afford meaningful gratications (especially
over playing games oneself). Anecdotally, it has been assumed that
watching others play does not provide the same thrills and affor-
dances for escapism as playing games by oneself, as the spectator
has less agency over the events of the game. On the other hand,
watching others play may provide social gratications that are
commonly absent in a normal single-player experience. Indeed,
this social dimension has been shown to be of importance in pre-
vious research within video game streaming (Hamilton et al., 2014).
Much of the previous research related to spectating video games
has concentrated on competitive gaming, commonly called eSports
(Hamari &Sj
oblom, 2017). Research within eSports has indicated
the importance of knowledge acquisition, escapism, social inter-
action, sharing emotional connections and the competitive atmo-
sphere as important motivators (Cheung &Huang, 2011;Hamari &
Sj
oblom, 2017;Lee &Schoenstedt, 2011;Scholz, 2012;Weiss &
Schiele, 2013; Weiss, 2011). Within the limited research on video
game streaming that has been conducted, social interaction,
learning and entertainment have been shown to be important as-
pects of spectatorship (Hamilton et al., 2014; Kaytoue et al., 2012;
Shaw, 2013). However, none of the previous research conducted
has aimed to quantiably measure relationships between spectator
gratications and the use of streaming services. Therefore, in this
M. Sj
oblom, J. Hamari / Computers in Human Behavior xxx (2016) 1e122
Please cite this article in press as: Sj
oblom, M., &Hamari, J., Why do people watch others play video games? An empirical study on the
motivations of Twitch users, Computers in Human Behavior (2016), http://dx.doi.org/10.1016/j.chb.2016.10.019
study we are employing UG as a theoretical guideline to under-
standing and modelling the relationships between gratications
and media consumption.
2.3. Research model and hypotheses
Based on the theory framework of UG, we investigate the rela-
tionship between ve types of motivation and four distinct types of
usages related to the consumption of video game streams. These
usage types are: hours watched, streamers followed, streamers
watched, and streamers subscribed to. Hours watched indicates
how many hours per week users consume streaming content. It is
possible to follow particular broadcasters (or streamers as they are
commonly referred to), thus allowing them to be more easily found
by users. Streamers watched measures the amount of unique
streamers watched per week for an individual user. Users may pay a
fee of $5 to subscribe to an individual streamer, and this is our
fourth type of usage investigated. A subscription must be renewed
monthly to retain benets, and additionally, one user may sub-
scribe to not just one, but also a number of streamers. The common
benets for subscription include emoticons exclusive to sub-
scribers, a visual indicator of subscription status in the chat facility,
and the possibility to take part in events or rafes aimed solely at
subscribers.
Fig. 1 presents the research model based on these ve main
constructs and their relationship to the usage dependant variables.
The ve main UG constructs are depicted as latent variables. For
clarity, the four types of usage have been grouped into one in our
visualisation of the research model, as the relationships between
the UG needs and four types of usage are identical to more general
notions of usage.
Based on previous research which indicates the importance of
entertainment for media usage (Cheung &Huang, 2011; Hamilton
et al., 2014; Hanson &Haridakis, 2008; Papacharissi &
Mendelson, 2010), we consider hours watched to be particularly
affected by an increase in affective motivation. As the entertain-
ment aspect of affective motivation is surely provided by a certain
subset of streamers rather than all of the streamers, we expect
streamers followed and streamers watched tobe positively affected
by increased levels of affective motivation. We expect a smaller
effect on subscription than other types of usage as, compared tothe
other three forms, subscription is unlikely to offer enough of a
payoff compared to the cost involved for a person driven by affec-
tive motivations. Hence, we propose that increased levels of af-
fective motivation will predict an increased level of usage
(Hypothesis 1).
Learning and information seeking has been shown to be an in-
tegral motivator for usage in several online media contexts
(Hamilton et al., 2014; Papacharissi &Mendelson, 2010; Whiting &
Williams, 2013). We expect increased levels of cognitive motivation
to predict an increased level of usage (Hypothesis 2). Of the four
types of usage, we argue that hours watched will especially be
impacted by an increased level of cognitive motivation. By watch-
ing for a larger amount of hours, we hypothesise that the learning
experience crucial for cognitive motivation will be facilitated. We
expect the other three types of usage to also be positively impacted
by an increase in cognitive motivation.
We expect hours watched and streamers subscribed to be
positively affected by an increased level of personal integrative
motivation. In the context of video game streaming, we predict a
certain level of social interaction will be required to achieve a level
of personal integrative fullment, especially as we are focusing on
recognition received through the usage of the service. Hence, social
aspects such as streamers followed are expected to be positively
affected by an increase in personal integrative motivation. We
expect a small positive correlation with subscription as we see it
impacting upon received recognition on a certain level. Thus we
hypothesise that an increased level of personal integrative moti-
vation will predict an increased level of usage (Hypothesis 3).
Social integrative motivations have been shown to positively
impact the usage of online media and services in previous research
(Chen, 2011; Hamilton et al., 2014; Pai &Arnott, 2013; Scholz, 2012;
Sherry et al., 2006; Whiting &Williams, 2013). We expect the same
to be true within the context of video game streaming, and that
increased levels of social integrative motivation will predict an
increased level of usage (Hypothesis 4). We especially expect hours
watched and streamers followed to be positively impacted by an
increase in social integrative motivation. Subscription furthers so-
cial connections and fosters a sense of belonging within the video
game streaming community on many levels. As such, this is also the
motivational factor where we consider subscription to be the most
impacted by an increase in motivation level, as many of the benets
acquired via subscribing are tied to social aspects of the service
(Oestreicher-Singer &Zalmanson, 2013).
The notion of tension release and escape having an impact on
use is a topic brought up in previous motivation research within
online communities (Courtois, Mechant, De Marez, &Verleye,
2009; Hanson &Haridakis, 2008; Lin, 2002; Papacharissi &
Mendelson, 2010; Whiting &Williams, 2013). We hypothesise
that increased levels of tension release motivation will predict an
increased level of usage (Hypothesis 5). In particular, we expect that
hours watched will be positively impacted by an increase in tension
release motivation, and that streamers watched and streamers
followed will also be positively impacted. We expect a large cor-
relation with hours watched, as the tension release motivation is so
closely linked to achieving a sense of escape from everyday life.
However, we expect a small association with subscription, as it
should not be heavily impacted by tension release motives.
3. Methodology
3.1. Sampling
We piloted the study with 19 respondents and launched the
Table 1
UG need types (West &Turner, 2010, p. 398).
Need type Description Media examples
Cognitive Acquiring information, knowledge,
comprehension
Television (news), video (How to Install Ceramic Tile), movies (documentaries or lms based on history
e.g., The Other Boleyn Girl)
Affective Emotional, pleasant, or aesthetic experience Movies, television (sitcoms, soap operas)
Personal
integrative
Enhancing credibility, condence, and status Video (Speaking With Conviction)
Social
integrative
Enhancing connections with family, friends,
and so forth
Internet (e-mail, chat rooms, Listservs, IM)
Tension release Escape and diversion Television, movies, video, radio, Internet
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oblom, J. Hamari / Computers in Human Behavior xxx (2016) 1e12 3
Please cite this article in press as: Sj
oblom, M., &Hamari, J., Why do people watch others play video games? An empirical study on the
motivations of Twitch users, Computers in Human Behavior (2016), http://dx.doi.org/10.1016/j.chb.2016.10.019
nal survey on February 26th, 2015. At launch, the end date was
specied as the 21st of March, but this was later extended to the
23rd of March. As a participatory incentive, we offered the chance
to win one of six video games from the online store Steam, worth
50 USD or EUR, and a rafe was conducted among valid survey
responses after the survey had concluded. The survey was pre-
dominantly distributed through social media and social news sites
such as Reddit, Twitter and Facebook, as well as a few other forums
dedicated to games.
To help lter out invalid responses we omitted respondents who
incorrectly answered a check question as well as entries with
missing data and nally obtained 1091 valid responses to the sur-
vey, representing a 3.2% decrease in respondents from the raw data.
Amongst the valid responses, the average age was 22.9 years
(M ¼22, SD ¼5.9). Female respondents comprised 7.7% of our data.
Of our respondents, 93.2% reported that they had registered an
account for the Twitch service, had used the service for an average
of 22.1 months (M ¼21, SD ¼14.6) and 38.7% had acted as a
streamer at some point. The demographic details of the re-
spondents are displayed in greater detail in Table 2.
3.2. Measurement
Respondents watched an average of 11.0 h per week (M ¼7,
SD ¼12.1) and an average of 5.6 different streamers per week
(M ¼4, SD ¼5.0). Furthermore, respondents followed an average of
26.4 streamers (M ¼10, SD ¼52.1) and subscribed to an average of
0.6 streamers (M ¼0, SD ¼2.5). Table 3 shows the distributions of
dependent variables.
We looked at psychometric factors that can explain the behav-
iour patterns seen among our respondents, and in this section, we
present the psychometric scales used for our nine constructs. All
psychometric items used a 7-point Likert scale (1 indicating
strongly disagreeand 7 indicating strongly agree). All of the
psychometric items, along with their origin, can be found in
Appendix A.
To measure affective motivations, we used the perceived enjoy-
ment scale of Venkatesh (2000) and van der Heijden (2004, pp.
695e704). The phrase the systemwas replaced with Twitch
as applicable, and one additional item was added following the
phrasing convention. The cognitive motivations construct consisted
of two sub-constructs: information seeking about game products and
learning game strategies. For information seeking about game
products, the usefulness scale taken from van der Heijden (2004, pp.
695e704), originally used in the hedonic information seeking
context, was used as a base and modied accordingly. To build our
learning game strategies scale, items from the information seeking
scale by Papacharissi and Rubin (2000) along with an item taken
from the van der Heijden (2004, pp. 695e704) usefulness scale
were used. These items were modied to t the context of video
game streaming and learning strategies. For personal integrative
motivations, we used the recognition by peers scale from
Hernandez, Montaner, Sese, and Urquizu (2011). Within social
integrative motivations, the companionship scale introduced by
Smock, Ellison, Lampe, and Wohn (2011), and the shared emotional
connection scale used by Chavis, Lee, and Acosta (2008) were used.
Relating to tension release, the scales of escapism,relaxing enter-
tainment, and habitual pass time previously introduced by Smock
et al. (2011) were used.
3.3. Validity &reliability
The model-testing was conducted using component-based PLS-
SEM (Partial Least Squares Structural Equation Modelling) which is
Fig. 1. Research model.
M. Sj
oblom, J. Hamari / Computers in Human Behavior xxx (2016) 1e124
Please cite this article in press as: Sj
oblom, M., &Hamari, J., Why do people watch others play video games? An empirical study on the
motivations of Twitch users, Computers in Human Behavior (2016), http://dx.doi.org/10.1016/j.chb.2016.10.019
considered to be more suitable for prediction-oriented studies such
as the present study (Anderson &Gerbing, 1988; Chin, Marcolin, &
Newsted, 2003). Convergent validity was metsince the AVE, CR and
Alpha measures exceeded the recommended thresholds (Fornell &
Larcker, 1981; Nunnally, 1978). Discriminant validity was met, as
the square root of the AVE of each construct was larger than its
correlation to any other construct (Chin, 1988; Fornell &Larcker,
1981; J
oreskog &S
orbom, 1996), and each measurement item
had the highest loading with its corresponding construct. The re-
sults of these validations are displayed in Table 4. The validity of the
scales and their individual items can be found in Appendix B.
In order to reduce the likelihood of common method bias, we
randomized the order of the measurement items on the survey to
limit the respondent's ability to detect patterns between the items
(Cook, Campbell, &Day, 1979). Common method bias refers to a
situation where there is variance that is attributable to the mea-
surement method rather than to the constructs the measures repre-
sent(Podsakoff, MacKenzie, Lee, &Podsakoff, 2003, p. 879). The
sample size (N ¼1097) satises several different criteria for the
lower bounds of sample size for PLS-SEM (Anderson &Gerbing,
1988; Chin &Newstad, 1999; Westland, 2010).
4. Results
The model accounted for 25.8% of the variance for hours
watched, as well as 21.5% for streamers followed and 17% for
streamers watched. For subscriptions, the model only accounted for
3.7% of the variance (Fig. 2). Table 5 displays the results for each of
the ve types of motivation in relation to the four types of usage.
From the results we can see how the initial hypotheses are sup-
ported. In the following paragraphs we examine the results, using
the same notation for statistical signicance as used in Table 5
(
*
p<0.05,
**
p<0.01,
***
p<0.001).
For affective motivations, three of our four hypotheses were
supported, with positive relationships seen between the motiva-
tion and hours watched (H1a
b
¼0.144
**
), streamers watched (H1b
b
¼0.134
**
) and streamers followed (H1c
b
¼0.152
***
). The rela-
tionship to subscription was above the threshold level of 0.05 for
statistical signicance and thus we could not establish a relation-
ship (H1d
b
¼0.045).
For cognitive motivations, we found positive relations with
hours watched (H2a
b
¼0.089
**
) as well as streamers watched
(H3b
b
¼0.075
*
), thus supporting two of our hypotheses. Our re-
sults for streamers followed (H2c
b
¼0.007) and subscription (H2d
b
¼0.028) did not exceed the threshold for statistical signicance.
Coupled with the low correlation, this goes to show that no relevant
relationship was to be found.
The personal integrative motivations are of particular interest,
as we found an opposite relationship to our hypotheses both for
hours watched and streamers watched (H3a
b
¼0.177
***
, H3b
Table 2
Demographic distribution of survey.
Factor (unit) Value Factor (unit) Value
Gender (%) Male 92.3% Employment (%) Student 57.12%
Female 7.7% Full-time 22.45%
Age (years) Average 22.94 Part-time 8.49%
Median 22.00 Unemployed 10.31%
SD 5.87 Income ($) <10 000 56.48%
Education (%) None 0.18% 10 000e29 999 21.81%
Primary level 8.67% 30 000e49 999 11.41%
Secondary level 52.19% 50 000e69 999 5.11%
Upper level 38.96% 70 000e89 000 2.10%
90 000 up 3.10%
Table 3
Dependant variable grouping (range &percentage of whole).
Group Hours watched Streamers watched Streamers followed Streamers subscribed to
10e2 (21.6%) 0e2 (19.8%) 0e2 (20.2%) 0
23e5 (22.5%) 3 (18.7%) 3e6 (20.0%) 1þ
36e10 (25.0%) 4e5 (30.2%) 7e15 (21.9%)
4 10.5e20 (17.1%) 6e9 (15.0%) 16e39 (19.0%)
521þ(13.8%) 10þ(16.3%) 40þ(18.9%)
Table 4
Fornell-Larcker criteria (main constructs in bold - values marked with asterisk are expected to correlate strongly with mother construct).
AVE CR Alpha AFF COG: PROD COG: STRAT COG PI SI: COMP SI: SEC SI TR TR: DIST TR: ESC TR: RELAX
AFF 0.749 0.922 0.887 0.865
COG: PROD 0.773 0.931 0.901 0.431 0.879
COG: STRAT 0.761 0.927 0.895 0.486 0.438 0.872
COG 0.550 0.907 0.882 0.539 0.864
*
0.831
*
0.742
PI 0.715 0.909 0.867 0.474 0.423 0.310 0.436 0.846
SI: COMP 0.743 0.896 0.826 0.496 0.394 0.283 0.402 0.546 0.862
SI: SEC 0.612 0.887 0.840 0.631 0.452 0.326 0.463 0.685 0.614 0.782
SI 0.537 0.902 0.875 0.643 0.477 0.345 0.489 0.696 0.856
*
0.934
*
0.733
TR 0.541 0.921 0.904 0.722 0.455 0.406 0.509 0.455 0.643 0.557 0.660 0.735
TR: DIST 0.663 0.887 0.829 0.641 0.410 0.364 0.458 0.389 0.559 0.479 0.573 0.902
*
0.814
TR: ESC 0.723 0.886 0.808 0.487 0.363 0.289 0.387 0.406 0.596 0.458 0.572 0.817
*
0.603 0.850
TR: RELAX 0.788 0.917 0.865 0.731 0.405 0.393 0.471 0.395 0.526 0.510 0.576 0.875
*
0.680 0.591 0.887
M. Sj
oblom, J. Hamari / Computers in Human Behavior xxx (2016) 1e12 5
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oblom, M., &Hamari, J., Why do people watch others play video games? An empirical study on the
motivations of Twitch users, Computers in Human Behavior (2016), http://dx.doi.org/10.1016/j.chb.2016.10.019
b
¼0.105
**
). For the relationship between personal integrative
motivations and streamers followed, our hypothesis was supported
(H3c
b
¼0.091
*
), however, the relationship between the need type
and subscription was found to not be statistically signicant (H3d
b
¼0.033).
For social integrative motivations, our hypotheses for hours
watched (H4a ¼0.132
**
), streamers watched (H4b
b
¼0.120
*
),
streamers followed (H4c
b
¼0.213
***
) and subscriptions (H4d
b
¼0.150
**
) were all supported. Notably this is the only need type
that showed a statistically signicant correlation with subscription.
Finally, for tension release, our hypotheses for hours watched
(H5a
b
¼0.319
***
), streamers watched (H5b
b
¼0.217
***
) and
streamers followed (H5c
b
¼0.080
*
) were supported. Once again
we found the relationship between the need type and subscription
(H5d
b
¼0.001) not to be of statistical signicance.
5. Discussion &conclusions
In this study we sought to unravel the motivations for watching
others play video games on the internet, and to determine which of
those motivations would predict how much people watch, and how
many streamers they watch, follow and subscribe to. On a general
level, our results reveal that all ve classes of gratication (cogni-
tive, affective, social, tension release, and personal integrative)
were signicantly associated with the main outcome variables
related to how many hours and how many streamers individual
users watch. Interestingly however, we nd that personal integra-
tive gratications are negatively associated with these outcomes.
For streamer subscriptions (which entail costs), we nd that the
gratications investigated in the study hold less explanatory power,
and the only gratication that seems to explain subscribing
behaviour is that concerning social integrative motivations.
5.1. Theoretical implications
With the rise of user-generated content as one of the main forms
of contemporary media, as discussed earlier (Cha et al., 2007), we
nd the results of this study to offer deeper insight not only into
broadcast media consumption, but also into how consumers
approach games as a medium. The rise of user-generated content
and live broadcasting of smaller scale video productions, as seen in
the case of video game streaming on Twitch, shows no signs of
slowing down. This would indicate a larger paradigm shift in how
we consume media in our society. The social interaction aspect is, in
particular, facilitated by the fact that content is being broadcast in
real-time, in contrast to more traditional broadcast media such as
television. Games have traditionally been considered to be separate
from other forms of media such as television and lm (Dovey &
Kennedy, 2006). However, through the penetration and diffusion
of broadcasted game streams, games are becoming a more mani-
fold media and consequently seeping onto areas of media tradi-
tionally separate from games. The results of this study also indicate
the simple fact that games ll a very real role for gratications not
only through play, but also through spectating. This can clearly be
seen from the associations between motivation factors and hours
watched.
When examining the results more closely, tension release was
seen as being the strongest positive predictor of how many hours
users watched streams. This nding supports our initial hypothesis
and is in line with previous research concerning social media
(Whiting &Williams, 2013), YouTube (Hanson &Haridakis, 2008),
Facebook (Papacharissi &Mendelson, 2010), online services (Lin,
2002), eSports (Hamari &Sj
oblom, forthcoming 2017) and
Internet use (Courtois et al., 2009). Moreover, tension release seems
to be an important motivator when it comes to the other viewing
Fig. 2. Results (
*
p<0.05,
**
p<0.01,
***
p<0.001).
M. Sj
oblom, J. Hamari / Computers in Human Behavior xxx (2016) 1e126
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motivations of Twitch users, Computers in Human Behavior (2016), http://dx.doi.org/10.1016/j.chb.2016.10.019
indicators, as it has a positive impact on not just the number of
hours watched, but also on the numbers of streamers watched and
streamers followed. This demonstrates that users seeking to fulla
need for escape and diversion watch a larger amount of streams.
The fact that there is no signicant relationship between tension
release and subscription is, perhaps, unsurprising, as the act of
subscription does not necessarily directly affect the needs of ten-
sion release. Very few of the tangible benets acquired from sub-
scribing to a streamer help the user achieve escape or diversion.
Due to this lack of concrete incentives, it seems quite natural that
there is no signicant relationship between them. As mentioned
earlier in this study, the most common tangible benets obtained
from subscribing are access to streamer-specic chat emoticons
and obtaining a visual indicator next to the user name in the chat
functionality. Additionally, some streamers offer access to
subscriber-only content, such as the opportunity to play with the
streamer. In the context of the online music service Last.fm, activity
in the community was seen as a more important indicator of sub-
scription than the consumption of music (Oestreicher-Singer &
Zalmanson, 2013). Even though there are signicant differences
in subscribing to a service and subscribing to an individual
streamer, there are still similarities that help offer an explanation
for this phenomenon. If community activity is an important pre-
dictor, it stands to reason that the needs governed by the tension
release category would not directly affect subscription.
In regard to affective motivations, we nd three statistically
signicant associations. Firstly, there is a positive association be-
tween affective motivations and hours watched. This is in accor-
dance with our hypothesis and also supported by previous research
into streaming (Hamilton et al., 2014; Shaw, 2013), eSports (Cheung
&Huang, 2011), social media (Papacharissi &Mendelson, 2010;
Whiting &Williams, 2013), and video sharing websites (Cha,
2014; Hanson &Haridakis, 2008). The moderate size of the corre-
lation suggests a person might nd the enjoyment they are looking
for after a certain amount of usage of the service, after which
additional hours become superuous. In the case of YouTube, the
entertainment aspect was important for comedy news watching
(Hanson &Haridakis, 2008), and it can be argued that video game
streaming falls in a similar area of hedonic consumption. The
positive association of affective gratication on how many
streamers users watch was equally of moderate size. This could
indicate that watching a small number of different streamers,
rather than seeking out a larger number of streamers achieves the
enjoyment sought from the service. Another explanation could be
that the person in question has trouble nding a larger number of
streamers that produce content that they think is enjoyable. There
was also a positive association with streamers followed, and as
streamers followed and streamers watched are quite closely
related, it is not surprising to see that these associations were close
to each other in size. A person for whom affective motivations are
important might very well seek out the same particular streamers
time after time, both watching them and choosing to follow them.
As with tension release, no signicant relationship was found
between affective motivation and subscription. As previously dis-
cussed, the tangible benets offered by subscription serve as a
limiting factor to the types of needs that can be met through sub-
scription. In this context we could expect to nd some factors that
would contribute to the need for enjoyment and entertainment. For
example, we might consider the potential extra content of partic-
ipatory events for subscribers to contribute something positive to
the overall entertainment obtained by a user, and by subscribing
the user would then get more opportunities that could lead to
increased levels of entertainment. However, our results show only a
small correlation that was not statistically signicant, so we have to
conclude that this hypothetical correlation is not observable in our
sample.
Cognitive motivations show a small positive association with
hours watched. This positive association supports our original hy-
potheses of cognitive motivations, and is further supported by
previous research within the elds of video game streaming
(Hamilton et al., 2014), eSports (Hamari &Sj
oblom, forthcoming
2017) and social media (Papacharissi &Mendelson, 2010;
Whiting &Williams, 2013). We can see that in the quest for
knowledge and information, a user is driven to increased use of the
service, even if the increased usage is not large. One reason might
be that the value obtained subsides after a short while. It could be
that viewers driven by cognitive motivation seek fullment for
their need for information through other sources rather than
streams. As we asked respondents about their attitudes towards
learning about game strategies and information seeking about
game products, these are needs that can also be met through other
channels. Perhaps the live aspect of video game streaming is
detrimental to the learning experience compared to video re-
cordings of similar content which are available through services
such as YouTube. On the other hand, the live experience and social
interaction available between viewer and streamer allows for a
level of personalisation that is not possible with pre-recorded
material. Previously, learning about games has been shown to be
a major reason for starting to watch streams (Hamilton et al., 2014).
Based on this research and also the interviews conducted at the
start of this study, we expected learning to have one of the more
inuential correlations, but this was not indicated by our results.
A small positive association is observed for streamers watched.
Users for whom cognitive motivations are important might watch
only a selected amount of streamers that offer the information and
guidance that they are looking for. Interestingly, we observe no
signicant association for cognitive motivations on streamers fol-
lowed, and it is notable that this was the only motivation type that
did not show a signicant association with streamers followed. This
seems to indicate that viewers looking to learn might not follow
any more streamers than other users do. We might expect a posi-
tive association here as not all streamers will be equally enlight-
ening, and it would stand to reason that viewers who have
cognitive motivations would follow streamers that they feel teach
them something, as this in turn would help them meet their needs.
As with the previous motivations, we do not nd a signicant
Table 5
Results (
*
p<0.05,
**
p<0.01,
***
p<0.001).
R
2
Hours watched Streamers watched Streamers followed Subscriber
0.258 0.17 0.215 0.037
b
PCI
b
PCI
b
PCI
b
PCI
Affective 0.144
**
0.001 0.058e0.224 0.134
**
0.002 0.048e0.216 0.152
***
0.000 0.068e0.235 0.045 0.298 0.041e0.130
Cognitive 0.089
**
0.007 0.023e0.155 0.075
*
0.037 0.007e0.147 0.007 0.851 0.062e0.074 0.028 0.453 0.099e0.042
Personal integrative 0.177
***
0.000 0.248to0.107 0.105
**
0.008 0.180to0.030 0.091
*
0.021 0.016e0.169 0.033 0.407 0.044e0.112
Social integrative 0.132
**
0.004 0.047e0.220 0.120
*
0.020 0.021e0.222 0.213
***
0.000 0.126e0.302 0.150
**
0.002 0.055e0.242
Tension release 0.319
***
0.000 0.240e0.400 0.217
***
0.000 0.128e0.300 0.080
*
0.048 0.001e0.153 0.001 0.984 0.095e0.093
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oblom, M., &Hamari, J., Why do people watch others play video games? An empirical study on the
motivations of Twitch users, Computers in Human Behavior (2016), http://dx.doi.org/10.1016/j.chb.2016.10.019
association with subscribing, the reasoning behind this might be
similar to that which we have presented for the previous motiva-
tion types. The act of subscribing does commonly not offer tangible
cognitive benets. Certain streamers might have days when the
chat facility is only open to subscribers, and the streamers then
interact more actively. One might argue that this can facilitate a
teacher and student relationship that would be benecial for a
viewer driven by cognitive motivations, and hence they would be
inclined to subscribe. Especially for cognitive motivations, the
notion of perceived value might be of importance. Economic value
has been shown to have a signicant impact on the willingness to
pay for social networking sites (Vock, van Dolen, &de Ruyter, 2013).
If a person looks to streamers to supply them with information that
might save them money, then paying for the service might seem
counterintuitive. However, the extra value gained by subscribing
might not exceed the value obtained, and thus create a negative
value proposition for a person driven by cognitive needs.
Opposite to that which was expected, personal integrative mo-
tivations show a moderate negative correlation with hours
watched. As the scale instrument used to measure personal inte-
grative needs was solely focused on recognition, we attempt to
explain our ndings based on the need for recognition in the per-
sonal integrative context. This result is partly in line with previous
research in the context of online learning, specically between the
user and their peers (Hernandez et al., 2011). Even though video
game streaming is mostly a hedonistic activity, we feel that drawing
parallels to online learning is feasible when examining motivations
related to recognition, as there can still be a learning component
present. The negative relationship between personal integrative
motivations and hours watched seems to indicate that the level of
recognition obtained from using the service does not full under-
lying needs. Hence, individuals for whom these needs are impor-
tant use the service less than others, and these needs are likely
fullled through other channels. This may be explained by the fact
that the social dimension of a channel changes with its size
(Hamilton et al., 2014). A stream with a larger amount of viewers
also means more people participate in the chat functionality. For
popular streamers, the chat activity may become chaotic, and thus
eliminate any chance for civilized discourse. As much of obtaining
respect from other viewers and streamers relies on being able to
use the chat facility to display a certain level of understanding of
the game or subject at hand, this presents a clear problem. The scale
used in this study did not particularly distinguish between the
recognition obtained from streamers and other viewers. One might
argue that for certain individuals these two types of recognition are
markedly different, as has been shown to be the case when looking
at differences in motivational impact between peers and in-
structors (Hernandez et al., 2011).
As with hours watched, we also nd a moderate negative rela-
tionship between personal integrative motivations and streamers
watched. As seen with hours watched, it seems that watching a
larger number of different streamers does not contribute positively
to the level of perceived recognition obtained. The negative rela-
tionship might also be a sign that the person enjoys a very partic-
ular set of streamers, one that is quite small and not easily
supplemented by new streamers. A person seeking recognition
might have found a few select streamers that offer a high level of
interaction, as well as communities that offer support and
encouragement to other community members.
Surprisingly, we nd a small positive correlation between per-
sonal integrative motivations and streamers followed. This is
interesting, as for the other types of motivations studied we saw
correlations of similar size for streamers watched and streamers
followed, but here we nd correlations which work in the opposite
direction for the same two categories. One explanation for this
might be that while people for whom recognition is important
might watch only a small number of streamers, they still follow a
larger amount. This might result in the fact that although they
watch fewer hours per week than other users, they might want to
have a larger selection of appropriate streamers to watch. Following
a slightly larger amount of different streamers that suit their needs
then allows these users to pick out those they wish to watch during
the limited time they spend on the service.
We do not nd a signicant relationship between personal
integrative motivations and subscription. This is somewhat unex-
pected, as compared to other types of motivations, subscription
offers some benets which are more closely related to personal
integrative needs. We argue that by subscribing to a particular
streamer, the person in question then belongs to a more exclusive
social clubof sorts. Belonging to this clique, the person would then
receive positive reinforcement from the other subscribers. Perhaps
the lack of association here goes to show that this is not the case,
and that the subscriber community for a given stream is not of great
importance when it comes to receiving recognition.
We nd that social integrative motivations correlate with hours
watched on a moderate level. This supports our initial hypothesis
and stands to reason, as using the service for more hours per week
clearly gives a person more opportunities to interact with the rest
of the Twitch community. This result is further supported by pre-
vious research in video game streaming and eSports (Hamilton
et al., 2014; Scholz, 2012), social media (Chen, 2011; Pai &Arnott,
2013; Papacharissi &Mendelson, 2010; Whiting &Williams,
2013), and playing video games (Sherry et al., 2006). These added
opportunities for interaction can then translate into positive ex-
periences which full the needs encompassed by social integrative
motivations. We might expect the quality of interaction to be of
importance for a person for whom social integrative motivations
are signicant, but we also notice that the pure number of
streamers watched is of importance. However, the correlation is
still not very large, signifying that a large number of streamers
watched is not necessarily valuable in itself.
The correlation with streamers followed is slightly larger than
the previous two, indicating that people for whom social integra-
tive motives are important, appreciate the ability to follow
streamers. Having a larger group of potential streamers to choose
from gives more exibility in choosing ones the person nds
particularly interesting. This added exibility might then translate
to a higher level of social involvement, as the person can choose
streams which offer the largest social aspect from a selected list.
Social integrative motivations are the sole types of motivation
where we nd a signicant association with subscription, albeit a
moderate one. This indicates that the feeling a person gains from
supporting a streamer is an important factor in fullling their social
integrative needs. By supporting a streamer and following them,
the person can develop a deeper involvement with the community
and feel involved in a larger part of the communities shared ex-
periences. As subscription activity shows up as an automated
message in the chat facility, we observed that other viewers often
offer positive encouragement to new and renewing subscribers.
The impact of social factors on choosing to subscribe to a service
has been shown to be relevant for other online services
(Oestreicher-Singer &Zalmanson, 2013), thus strengthening our
view that social integrative motives are highly relevant to sub-
scription. Services offering paid subscriptions seldom offer users
the possibility to contribute directly to a single content producer,
and there is therefore potential for a higher level of social grati-
cation to be obtained through subscription. In the context of video
game streaming, when a user subscribes to a streamer, he or she
can feel good about supporting that particular streamer and
perhaps enabling them to continue producing content.
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oblom, M., &Hamari, J., Why do people watch others play video games? An empirical study on the
motivations of Twitch users, Computers in Human Behavior (2016), http://dx.doi.org/10.1016/j.chb.2016.10.019
5.2. Practical implications
Based on the broader observations and theoretical implications
presented in the previous section, we would like to further our
discussion of gratications within video game streaming through
some recommendations related to practical implications.
From a game development standpoint, the fact that games are
being turned from sole playing experiences into spectator experi-
ences increases the amount of facets that should be taken into
account in game design. Content creators are increasingly turning
to games as a new media of conveying a message to their audience
in the form of both video game streaming and videos on services
such as YouTube. These game videos serve as a framework for a
host of new actors in the media landscape, following a more general
paradigm shift away from large scale productions and towards
user-generated content. Developers are advised to keep in mind
that their products are thus employed in a variety of communica-
tive purposes beyond playing. Streaming has already been shown
to have a signicant impact when it comes to game sales
(Hernandez, 2016), and by taking into account the spectator
element of games, companies can achieve a competitive advantage
over each other if they can capitalize on this development in a
timely manner. How to make a game more appealing for spectators
is no easy task, and not an activity this paper aimed to answer,
however, we suggest to investigate particularly the social aspects
related to both gameplay and game spectatorship as also prompted
by the results of the present study.
As was noted earlier in this paper, cognitive motivations, such as
information seeking, was not signicantly associated with how
much people watch streams, while at the same time respondents
did report receiving related gratication from stream consumption.
This might imply that viewers seek to watch streams for other
reasons but might receive cognitive gratication as a by-product.
That is to say, users primarily use other forms of media to fully
full these cognitive needs. Video sharing services such as YouTube
feature a host of videos detailing how to play games, and perhaps a
major contributor is the fact that users can pause videos, rewind
and watch sections multiple times. This facet is challenging when it
comes to live broadcasting, and perhaps rather than only offer ar-
chives of live streams, streaming services could look to the possi-
bility of incorporating tools for making communication between
streamers and spectators more seamless. This might then, in turn,
facilitate learning in the form of something more akin to a teacher-
student relationship. Perhaps another angle to approach this, for
stream services, is to highlight particularly educative streams in an
effort to make it easier for users to nd them.
Perhaps the strongest results of the present study highlight the
fact that social factors are an immensely important aspect of the
consumer experience of streaming. The results show that feeling a
sense of community in the watching experience not only increases
how much people watch streams, but perhaps more importantly,
was also the strongest determinant of following streamers and
subscribing. Therefore, it seems clear that game developers,
streaming platform developers and streamers alike would be
strongly advised to attempt to increase the degree to which the
viewers experience communality and sense of belonging. This is
particularly true for streamers, as attracting highly dedicated
viewers that then transition into subscribers, generating revenue, is
something of great interest for the streamer in question. Currently,
social aspects on streaming services are mainly facilitated through
chat functionalities, following and subscribing, although many
streamers take to using ancillary services, such as social media and
private discussion groups, to maintain their streaming community.
This goes to show that merely having a chat is not enough for many
viewers that are highly dedicated, demonstrating the need for
streaming service developers to further integrate tools and services
for social interaction into the core activity of stream spectating.
This strong positive effect of community does, however, not
carry over to personal integrative motivations and receiving
recognition. The negative association found for building one's
reputation and watching streams would indicate that there is work
to be done within that domain for service developers. While not all
forms of media can expect to obtain high levels of gratications
across the board, we argue that by focusing on the problems behind
this negative association, one could improve the situation. As dis-
cussed previously, a problematic dynamic seems to exist between
the amount of viewers and the potential ability to interact in a
meaningful way for receiving recognition. That is to say, when
channels increase in size, communication through chat becomes
more problematic as there is more communication noise generated
by the increased number of chat messages. Interestingly, building
reputation has been shown to positively affect continued use for
video sharing sites such as YouTube (Chiang &Hsiao, 2014), so
perhaps through the usage of the recommendation systems used in
services like YouTube, streaming services could also increase the
personal integrative gratications obtained from watching streams.
5.3. Future research directions
The topic of this study offers many potentially fruitful avenues
for further research. While this study investigated the motivational
types commonly used in UG research, there remain factors worthy
of study in order to build a deeper understanding of the video game
streaming phenomena. For example, comparisons of motivation
levels between various types of demographic factors, such as
gender, country of residence, education and income could be
valuable. Investigating differences between service usage habits
could also prove valuable. For example, we could identify differ-
ences in player motivations in different types of game genres. Game
genres are inherently different, as some games are highly
competitive, while others offer more of a free-form playing expe-
rience. We believe this not only impacts on the playing experience,
but is also reected in the viewing experience. Subscriptions are a
signicant indicator of service usage and indicate a willingness to
pay for content, however, this study was not able to obtain a high
level of prediction when it comes to subscription motivations.
Therefore, it would also be of great interest to further investigate
the types of motivations that drive subscription behaviour.
Furthermore, the personal integrative motivations investigated
in this study were limited to recognition. In future research it might
be benecial to investigate the inclusion of more than one scale for
identifying different aspects of personal integrative motivations,
especially given the results produced by this study, where we found
a negative association between recognition and hours watched.
Disclosure statement
No competing nancial interests exist.
Acknowledgements
The research has been carried out as part of research projects
(40009/16, 40111/14) funded by the Finnish Funding Agency for
Innovation (TEKES).
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oblom, M., &Hamari, J., Why do people watch others play video games? An empirical study on the
motivations of Twitch users, Computers in Human Behavior (2016), http://dx.doi.org/10.1016/j.chb.2016.10.019
Appendix A. Psychometric items
Appendix B. Discriminant validity of psychometric items,
corresponding scale values bolded
Item Related UG need type Scale origin AVE CR Mean
Enjoyment (ENJ) Affective Venkatesh, 2000 0.75 0.92 5.42
ENJ_1: I nd using Twitch to be enjoyable.
ENJ_2: Using Twitch is exciting.
ENJ_3: I have fun using Twitch.
ENJ_4: Using Twitch is entertaining
Information seeking about game products (IS_PROD) Cognitive van der Heijden, 2004 0.77 0.93 4.57
IS_PROD_1: Using Twitch, I can better decide which game I want to play than in the past
IS_PROD_2: Using Twitch, I am better informed about new games I consider playing
IS_PROD_3: Using Twitch, I nd games I would not otherwise have found.
IS_PROD_4: Using Twitch, I can better decide whether I want to play a particular game or not
Learning about game strategies (LSTRA) Cognitive Papacharissi &Rubin, 2000 0.76 0.93 5.3
LSTRA_1: Watching Twitch, I am better informed about new game strategies
LSTRA_2: Watching Twitch helps me get information on learning to play games.
LSTRA_3: Watching Twitch helps me look for information on game tricks.
LSTRA_4: Watching Twitch helps me see what game tactics are out there.
Recognition (REC) Personal integrative Hernandez et al., 2011 0.72 0.91 4.1
REC_1: I like when other Twitch users take my comments into account
REC_2: I feel good when my comments prove to other Twitch users that I have knowledge about the game being played.
REC_3: I try that my comments improve my reputation among other Twitch users.
REC_4: I like when streamers on Twitch take my suggestions into consideration.
Companionship (COMP) Social integrative Smock et al., 2011 0.74 0.90 3.57
COMP_1: Using Twitch, I don't have to be alone.
COMP_2: I use Twitch when there's no one else to talk or be with
COMP_3: Using Twitch makes me feel less lonely
Shared emotional connection (SEC) Social integrative Chavis et al., 2008 0.61 0.89 3.81
SEC_1: It is very important to me to be a part of the Twitch community.
SEC_2: I spend time with other Twitch community members a lot and enjoy spending time with them.
SEC_3: I expect to be a part of the Twitch community for a long time.
SEC_4: Members of the Twitch community have shared important events together.
SEC_5: Members of the Twitch community care about each other.
Escape (ESC) Tension release Smock et al., 2011 0.72 0.89 4.18
ESC_1: Using Twitch, I can forget about school, work, or other things
ESC_2: Using Twitch, I can get away from the rest of my family or others
ESC_3: Using Twitch, I can get away from what I'm doing.
Distraction (DIST) Tension release Smock et al., 2011 0.66 0.89 5.03
DIST_1: Using Twitch is a habit, just something I do.
DIST_2: When I have nothing better to do, I use Twitch.
DIST_3: Using Twitch passes the time away, particularly when I'm bored
DIST_4: Using Twitch gives me something to do to occupy my time.
Relaxation (RELAX) Tension release Smock et al., 2011 0.79 0.92 5.38
RELAX_1: Watching Twitch allows me to unwind.
RELAX_2: Watching Twitch relaxes me
RELAX_3: Watching Twitch is a pleasant rest
ENJ IS_PROD LSTRA REC COMP SEC DIST ESC RELAX
COMP_1 0.454 0.345 0.240 0.518 0.900 0.590 0.456 0.522 0.456
COMP_2 0.409 0.293 0.257 0.388 0.796 0.443 0.552 0.507 0.438
COMP_3 0.419 0.376 0.239 0.495 0.885 0.545 0.452 0.513 0.468
DIST_1 0.485 0.339 0.316 0.313 0.440 0.391 0.754 0.479 0.490
DIST_2 0.467 0.309 0.303 0.267 0.425 0.340 0.825 0.406 0.511
DIST_3 0.596 0.368 0.312 0.350 0.500 0.425 0.882 0.564 0.645
DIST_4 0.529 0.316 0.255 0.332 0.451 0.403 0.789 0.507 0.557
ENJ_1 0.855 0.353 0.418 0.352 0.369 0.480 0.555 0.382 0.599
ENJ_2 0.804 0.362 0.418 0.462 0.462 0.592 0.482 0.451 0.584
ENJ_3 0.908 0.384 0.417 0.417 0.466 0.585 0.584 0.420 0.686
ENJ_4 0.890 0.393 0.432 0.417 0.424 0.532 0.591 0.440 0.655
ESC_1 0.431 0.293 0.250 0.337 0.518 0.389 0.563 0.874 0.536
ESC_2 0.333 0.298 0.214 0.357 0.491 0.390 0.415 0.785 0.418
ESC_3 0.467 0.335 0.269 0.346 0.513 0.392 0.548 0.888 0.543
IS_PROD_1 0.384 0.895 0.398 0.395 0.381 0.405 0.381 0.324 0.351
IS_PROD_2 0.387 0.916 0.466 0.378 0.334 0.414 0.351 0.311 0.347
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References
Abercrombie, N., & Longhurst, B. J. (2007). The Penguin dictionary of media studies.
Penguin.
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A
review and recommended two-step approach. Psychological Bulletin, 103(3),
411e423.
Baran, J. S., & Davis, K. D. (2006). Mass communication theory: Foundation and future.
Boyd, d. M., & Ellison, N. B. (2007). Social network Sites: Denition, history, and
scholarship. Journal of Computer-Mediated Communication, 13,210e230.
Cha, J. (2014). Usage of video sharing websites: Drivers and barriers. Telematics and
Informatics, 31,16e26.
Cha, M., Kwak, H., Rodriguez, P., Ahn, Y., & Moon, S. (2007). I tube, you tube,
everybody tubes: Analyzing the world's largest user generated content video
system. In Proceedings of the 7th ACM SIGCOMM conference on internet mea-
surement (pp. 1e14). ACM.
Chavis, D. M., Lee, K. S., & Acosta, J. D. (2008). The sense of community (SCI) revised:
The reliability and validity of the SCI-2. In 2nd international community psy-
chology conference. Lisbon.
Chen, G. M. (2011). Tweet this: A uses and gratications perspective on how active
twitter use graties a need to connect with others. Computers in Human
Behavior, 27, 755e762 .
Cheung, G., & Huang, J. (2011). Starcraft from the stands: Understanding the game
spectator. In CHI '11 proceedings of the SIGCHI conference on human factors in
computing systems (pp. 763e772). New York: ACM.
Chiang, H.-S., & Hsiao, K.-L. (2014). YouTube stickiness: The needs, personal, and
environmental perspective. Internet Research, 25(1), 85e106.
Chin, W. W. (1988). The partial least squares approach for structural equation
modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp.
295e336). Lawrence Erlbaum Associates.
Chin, W. W., Marcolin, B. L., & Newsted, P. R. (20 03). A partial least squares latent
variable modeling approach for measuring interaction effects: Results from a
Monte Carlo simulation study and an electronic-mail emotion/adoption study.
Information systems research, 14(2), 189e217.
Chin, W. W., & Newstad, P. R. (1999). Structural equation modeling analysis with
small samples using partial least squares. In R. Hoyle (Ed.), Statistical strategies
for small sample research (pp. 307e341). Thousand Oaks: Sage Publications.
Cook, T. D., Campbell, D. T., & Day, A. (1979). Quasi-experimentation: Design &
analysis issues for eld settings (Vol. 351). Boston: Houghton Mifin.
Courtois, C., Mechant, P., De Marez, L., & Verleye, G. (2009). Gratications and
seeding behavior of online adolescents. Journal of Computer-Mediated Commu-
nication, 15,109e137.
Dovey, J., & Kennedy, H. W. (2006). Game cultures: Computer games as new media:
Computer games as new media. McGraw-Hill Education.
Ewalt, D. M. (2014, 1 16). How big is Twitch's Audience? Huge. Retrieved 6 6, 2016,
from Forbes: http://www.forbes.com/sites/davidewalt/2014/01/16/twitch-
streaming-video-audience-growth/.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with un-
observable variables and measurement error. Journal of Marketing Research,
18(1), 39e50.
Gelman, A. D., Haln, S., & Willinger, W. (1991). On buffer requirements for store-
and-forward video on demand service circuits. In Global telecommunications
conference (pp. 976e980). Phoenix: IEEE.
Grundberg, S., & Hansegard, J. (2014). YouTube's biggest draw plays games, earns $4
million a year. Retrieved October 29, 2015, from The Wall Street Journal: http://
www.wsj.com/articles/youtube-star-plays-videogames-earns-4-million-a-year-
1402939896.
Hamari, J., & Sj
oblom, M. (2017). What is eSports and why do people watch it?
Internet Research, 27(2). http://dx.doi.org/10.1108/IntR-04-2016-0 085.
Hamilton, W. A., Garretson, O., & Kerne, A. (2014). Streaming on twitch: Fostering
participatory communities of play within live mixed media. In CHI '14 pro-
ceedings of the SIGCHI conference on human factors in computing systems (pp.
1315e1324). New York: ACM.
Hanson, G., & Haridakis, P. (2008). YouTube users watching and sharing the news: A
uses and gratications approach. The Journal of Electronic Publishing, 11(3).
van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS
Quarterly.
Hernandez, D. (2016). Game creator success on Twitch: Hard numbers. Retrieved
6.10.2016 from Twitch: https://blog.twitch.tv/https-blog-twitch-tv-game-
creator-success-on-twitch-hard-numbers-688154815817.
Hernandez, B., Montaner, T., Sese, F. J., & Urquizu, P. (2011). The role of social mo-
tivations in e-learning: How do they affect usage and success of ICT interactive
tools? Computers in Human Behavior, 6, 2224e2232.
Ishii, K. (2006). Implications of Mobility: The Uses of Personal Communication
Media in Everyday Life. Journal of Communication, 56(2), 346e365.
Jansz, J., & Martens, L. (2005). Gaming at a LAN event: The social context of playing
video games. New media &society, 7(3), 333e355.
Johnson, P. R., & Yang, S.-U. (2009). Uses and gratications of Twitter: An exami-
nation of user motives and satisfaction of Twitter use. In Communication tech-
nology division of the annual convention of the association for education in
journalism and mass communication. Boston.
Joinson, A. N. (2008). Looking at, looking up or keeping up with people? Motives
and use of Facebook. In Proceedings of the SIGCHI conference on human factors in
computing systems (pp. 1027e1036). ACM.
J
oreskog, K. G., & S
orbom, D. (1996). LISREL 8: User's reference guide. Scientic
Software International.
Katz, E., Blumler, J. G., & Gurevitch, M. (1973). Uses and gratications research. The
Public Opinion Quarterly, 37(4), 509e523.
Katz, E., Blumler, J. G., & Gurevitch, M. (1974). Utilization of mass communication by
the individual. In E. Katz, & J. G. Blumler (Eds.), The uses of mass communications:
Current perspectives on gratications research (pp. 19e34). Bevery Hills, CA: Sage.
Katz, E., Gurevitch, M., & Haas, H. (1973). On the use of the mass media for
important things. American Sociological Review, 38(2), 164e181.
Kaytoue, M., Silva, A., Cerf, L., Meira, W. J., & Raïssi, C. (2012). Watch me playing, I am
a professional: A rst study on video game live streaming. In WWW '12 com-
panion proceedings of the 21st international conference companion on World Wide
Web (pp. 1181e1188). New York: ACM.
Ko, H., Cho, C.-H., & Roberts, M. S. (2005). Internet uses and gratications: A
structural equation model of interactive advertising. Journal of Advertising,
34(2), 57e70.
Krcmar, M., & Greene, K. (1999). Predicting exposure to and uses of television
violence. Journal of Communication, 49(3), 24e45.
Lange, P. G. (2007). Publicly private and privately Public: Social networking on
YouTube. Journal of Computer-Mediated Communication, 13,361e380.
LaRose, R., & Eastin, M. S. (2004). A social cognitive theory of Internet uses and
gratications: Toward a new model of media attendance. Journal of Broadcasting
&Electronic Media, 24(3), 358e377.
Lee, D., & Schoenstedt, L. J. (2011). Comparison of eSports and traditional sports
consumption motives. The ICHPER-SD Journal of Research in Health, Physical
Education, Recreation, Sport &Dance, 6(2), 39.
Lin, C. A. (2002). Perceived grati
®
cations of online media service use among po-
tential users. Telematics and Informatics, 19,3e19.
McLuhan, M. (1994). Understanding media: The extensions of man. MIT Press.
Needleman, S. E. (2015, 1 29). Twitch's viewers reach 100 million a month. Retrieved 6
6, 2016, from The Wall Street Journal: http://blogs.wsj.com/digits/2015/01/29/
twitchs-viewers-reach-100-million-a-month/.
Newman, J. (2004). Videogames. New York: Routledge.
Nunnally, J. C. (1978). Psychometric theory. New York: McGraw-Hill.
Oestreicher-Singer, G., & Zalmanson, L. (2013). Content or community? a digital
(continued )
ENJ IS_PROD LSTRA REC COMP SEC DIST ESC RELAX
IS_PROD_3 0.372 0.800 0.280 0.360 0.351 0.415 0.356 0.318 0.377
IS_PROD_4 0.376 0.900 0.377 0.357 0.323 0.361 0.358 0.326 0.356
LSTRA_1 0.450 0.354 0.904 0.249 0.225 0.272 0.324 0.241 0.347
LEARN_2 0.429 0.472 0.877 0.302 0.280 0.318 0.326 0.261 0.364
LSTRA_3 0.397 0.367 0.822 0.295 0.287 0.295 0.319 0.273 0.339
LSTRA_4 0.417 0.323 0.884 0.233 0.193 0.252 0.300 0.233 0.319
REC_1 0.431 0.378 0.252 0.903 0.483 0.614 0.358 0.340 0.360
REC_2 0.353 0.307 0.268 0.814 0.429 0.507 0.280 0.339 0.298
REC_3 0.365 0.337 0.227 0.806 0.459 0.629 0.274 0.318 0.292
REC_4 0.448 0.403 0.305 0.856 0.471 0.554 0.398 0.376 0.381
RELAX_1 0.537 0.372 0.274 0.317 0.445 0.416 0.530 0.532 0.850
RELAX_2 0.688 0.348 0.368 0.362 0.482 0.460 0.634 0.533 0.915
RELAX_3 0.713 0.359 0.400 0.370 0.472 0.480 0.643 0.510 0.896
SEC_1 0.466 0.364 0.225 0.613 0.570 0.848 0.369 0.387 0.398
SEC_2 0.394 0.343 0.193 0.597 0.509 0.823 0.309 0.334 0.339
SEC_3 0.666 0.412 0.347 0.494 0.462 0.747 0.537 0.426 0.512
SEC_4 0.511 0.343 0.309 0.476 0.408 0.725 0.387 0.355 0.396
SEC_5 0.446 0.308 0.219 0.484 0.439 0.762 0.283 0.290 0.359
M. Sj
oblom, J. Hamari / Computers in Human Behavior xxx (2016) 1e12 11
Please cite this article in press as: Sj
oblom, M., &Hamari, J., Why do people watch others play video games? An empirical study on the
motivations of Twitch users, Computers in Human Behavior (2016), http://dx.doi.org/10.1016/j.chb.2016.10.019
business strategy for content providers in the social age. MIS Quarterly, 37(2),
591e616.
Pai, P., & Arnott, D. C. (2013). User adoption of social networking sites: Eliciting uses
and gratications through a meanseend approach. Computers in Human
Behavior, 29, 1039e1053.
Papacharissi, Z., & Mendelson, A. (2010). 12 toward a new (er) sociability: Uses,
gratications and social capital on Facebook. In S. Papathanassopoulos (Ed.),
Media perspectives for the 21st century (pp. 212e230). Routledge.
Papacharissi, Z., & Rubin, A. M. (2000). Predictors of internet use. Journal of
Broadcasting &Electronic Media, 44(2), 175e19 6.
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common
method biases in behavioral research: A critical review of the literature and
recommended remedies. Journal of applied psychology, 88(5), 879e903.
Quandt, T., Van Looy, J., Vogelsang, J., Elson, M., Ivory, J. D., Consalvo, M., et al. (2015).
Digital games research: A survey study on an emerging eld and its prevalent
debates. Journal of Communication.http://dx.doi.org/10.1111/jcom.12182.
Rubin, A. M. (2002). The uses-and-gratications perspective of media effects. In
J. Bryant, & D. Zillmann (Eds.), Media effects: Advances in theory and research.
Mahwah, NJ: Lawrence Erlbaum Associates Publishers.
Ruggiero, T. E. (2000). Uses and gratications theory in the 21st century. Mass
Communication &Society, 3(1), 3e37.
Schmitt, K. L., Woolf, K. D., & Anderson, D. R. (2003). Viewing the Viewers: Viewing
behaviors by children and adults during television programs and commercials.
Journal of Communication, 53(2), 265e281.
Scholz, T. M. (2012). New broadcasting ways. In IPTV eThe case of the starcraft
broadcasting scene. World media economics &management conference.
Shaw, A. (2013). E-Sport spectator motivation. Fairfax: George Mason University.
Sherry, J. L., Lucas, K., Greenberg, B. S., & Lachlan, K. (2006). Video game uses and
gratications as predictors of use and game preference. In P. Vorderer, &
J. Bryant (Eds.), Playing video games: Motives, responses, and consequences (pp.
213e224).
Smock, A. D., Ellison, N. B., Lampe, C., & Wohn, D. Y. (2011). Facebook as a toolkit: A
uses and gratication approach to unbundling feature use. Computers in Human
Behavior, 27(6), 2322e2329.
Twitch. (2015). 2014 retrospective. Retrieved 6 6, 2016, from Twitch: https://www.
twitch.tv/year/2014.
Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control,
intrinsic motivation, and emotion into the technology acceptance model. In-
formation systems research, 11(4), 342e365.
Vock, M., van Dolen, W., & de Ruyter, K. (2013). Understanding willingness to pay
for social network sites. Journal of Service Research, 16(3), 311e325.
Wang, Q., Fink, E. L., & Cai, D. A. (2008). Loneliness, gender, and parasocial inter-
action: A uses and gratications approach. Communication Quarterly, 56(1),
87e109.
Wang, Z., & Tchernev, J. M. (2012). The ‘‘Myth’’ of media Multitasking: Reciprocal
dynamics of media multitasking, personal needs, and gratications. Journal of
Communication, 62(3), 493e513.
Weiss, T. (2011). Fullling the needs of eSports consumers: A uses and gratications
perspective. In Proceedings of the 24th bled eConference eFuture: Creating so-
lutions for the individual, organisations and society. Bled, Slovenia.
Weiss, T., & Schiele, S. (2013). Virtual worlds in competitive contexts: Analyzing
eSports consumer needs. Electronic Markets, 23(4), 307e316.
Westland, J. C. (2010). Lower bounds on sample size in structural equation
modeling. Electronic Commerce Research and Applications, 9(6), 476e487.
West, R. L., & Turner, L. H. (2010). Introducing communication Theory: Analysis and
application. Boston, MA: McGraw-Hill.
Whiting, A., & Williams, D. (2013). Why people use social media: A uses and
gratications approach. Qualitative Market Research: An International Journal,
16(4), 362e369.
Wu, J.-H., Wang, S.-C., & Tsai, H.-H. (2010). Falling in love with online games: The
uses and gratications perspective. Computers in Human Behavior, 26,
1862e1871.
M. Sj
oblom, J. Hamari / Computers in Human Behavior xxx (2016) 1e1212
Please cite this article in press as: Sj
oblom, M., &Hamari, J., Why do people watch others play video games? An empirical study on the
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  • ... Looking over the player's shoulder has always been a crucial part of our digital gaming experience. ere are manifold reasons to watch others play, such as entertainment or learning game strategies [50]. roughout history, the habit of observing players has evolved from casual get-togethers in domestic se ings to highly a ended live tournaments with thousands of on-site supporters and even more remote viewers. ...
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    Watching others play is a key ingredient of digital games and an important aspect of games user research. However, spectatorship is not very popular in virtual reality, as such games strongly rely on one's feelings of presence. In other words, the head-mounted display creates a barrier between the player and the audience. We contribute an alternative watching approach consisting of two major components: a dynamic view frustum that renders the game scene from the current spectator position and a one-way mirror in front of the screen. This mirror, together with our silhouetting algorithm, allows seeing the player's reflection at the correct position in the virtual world. An exploratory survey emphasizes the overall positive experience of the viewers in our setup. In particular, the participants enjoyed their ability to explore the virtual surrounding via physical repositioning and to observe the blended player during object manipulations. Apart from requesting a larger screen, the participants expressed a strong need to interact with the player. Consequently, we suggest utilizing our technology as a foundation for novel playful experiences with the overarching goal to transform the passive spectator into a collocated player.
  • ... More specifically, scholars have prompted and largely facilitated in-depth discussions regarding the definition of esports and the qualification of esports as sport and sport management scholarship (Cunningham et al., 2018;Hallmann and Giel, 2018). Researchers have also empirically examined esports consumer behavior in different contexts such as esports gameplay (Seo, 2016;, online esports media consumption (Sjöblom and Hamari, 2017;Qian et al., 2019), and esports event attendance (Pizzo et al., 2018). ...
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    On the last decade, videogames streams have reached incredible numbers and are becoming more important every day. With the advancements of the internet, videogames are no longer local and can be played by people in different places as long as there is an internet connection between them. In this field, we have seen the recent innovations in videogames, especially in the so-called eSports category, with the growing adherence of fans worldwide and several events of great magnitude. In this study, we analysed and selected eSports aspects based on the related literature and videogame development specialists. The selected aspects are Objectives and Rules, Competitiveness, Interface Information, Visual Identification in Players and Teams, Stream Content and Communication Groups. Through a social network (LinkedIn), indie videogame developers were consulted (n = 11), thus answering 10 questions about how the above aspects should be addressed, and whether changes should be made to improve videogames in a general context. We found out that the presentation of the objectives and rules of the game may need changes across different game genres and developers may use competitiveness to entertain players and spectators. About interfaces, customizable and increase the viewer’s experience is crucial to keep differences between playing and watching screens. The use of external groups and media is important to improve the communication between viewers, players and developers, as the use of visual elements can be useful to create marketing identities with customers.
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    The statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined. A drawback of the commonly applied chi square test, in addition to the known problems related to sample size and power, is that it may indicate an increasing correspondence between the hypothesized model and the observed data as both the measurement properties and the relationship between constructs decline. Further, and contrary to common assertion, the risk of making a Type II error can be substantial even when the sample size is large. Moreover, the present testing methods are unable to assess a model's explanatory power. To overcome these problems, the authors develop and apply a testing system based on measures of shared variance within the structural model, measurement model, and overall model.
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    Provides a nontechnical introduction to the partial least squares (PLS) approach. As a logical base for comparison, the PLS approach for structural path estimation is contrasted to the covariance-based approach. In so doing, a set of considerations are then provided with the goal of helping the reader understand the conditions under which it might be reasonable or even more appropriate to employ this technique. This chapter builds up from various simple 2 latent variable models to a more complex one. The formal PLS model is provided along with a discussion of the properties of its estimates. An empirical example is provided as a basis for highlighting the various analytic considerations when using PLS and the set of tests that one can employ is assessing the validity of a PLS-based model. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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    Purpose: The purpose of this paper is to investigate why do people spectate eSports on the internet. The authors define eSports (electronic sports) as “a form of sports where the primary aspects of the sport are facilitated by electronic systems; the input of players and teams as well as the output of the eSports system are mediated by human-computer interfaces.” In more practical terms, eSports refer to competitive video gaming (broadcasted on the internet). Design/methodology/approach: The study employs the motivations scale for sports consumption which is one of the most widely applied measurement instruments for sports consumption in general. The questionnaire was designed and pre-tested before distributing to target respondents (n=888). The reliability and validity of the instrument both met the commonly accepted guidelines. The model was assessed first by examining its measurement model and then the structural model. Findings: The results indicate that escapism, acquiring knowledge about the games being played, novelty and eSports athlete aggressiveness were found to positively predict eSport spectating frequency. Originality/value: During recent years, eSports (electronic sports) and video game streaming have become rapidly growing forms of new media in the internet driven by the growing provenance of (online) games and online broadcasting technologies. Today, hundreds of millions of people spectate eSports. The present investigation presents a large study on gratification-related determinants of why people spectate eSports on the internet. Moreover, the study proposes a definition for eSports and further discusses how eSports can be seen as a form of sports.
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    Digital games have become a popular form of media entertainment. However, it remains unclear whether a canon of accepted knowledge and research practices has emerged that may define an independent field of research. The present study is a collaborative effort to analyze the outlines of digital games research (DGR) through a survey among the membership of three institutionalized structures focusing on the study of digital games (ICA Game Studies IG, ECREA TWG Digital Games Research, and DiGRA). The study reveals relatively homogeneous viewpoints among games researchers, even regarding controversial aspects of digital games. It mirrors the mainstream scholarly views on contentious issues of a recently emerged field within communication studies.