Exploring streaming on Twitch: the role of Big Five
personality traits on motivations and Twitch-usage
Master’s Thesis
Graduate School of Communication
Communication Science: Entertainment Communication
Name: Fabian van der Vaart
Student ID: 10209905
Supervisor: mw. Dr. M. Timmers
2 Abstract
This study explored the relationship between Big Five personality traits and motivations on the usage of video game streams. Video game streaming have recently grown in popularity amongst adolescents and young adults as new form of media, as seen by the rise of Twitch. A survey (N = 630) was conducted amongst Twitch-users to find out which personality traits were predictors of motivations and predictors Twitch-usage. Regression analyses revealed that neuroticism, agreeableness and extraversion are predictors for various motivations. Extraversion is negatively associated with hours spent on Twitch, but extraversion and agreeableness are a positive predictor for spending behavior. Tension release and social integrative motivations emerged as the most important motivations of Twitch-usage. Theoretical and practical implications are discussed.
3 Watching other people play videogames through live internet broadcasts, commonly known as streaming, is a popular new form of entertainment that fuses the boundaries between gaming and television. Video game streaming has become immensely popular over the past few years and has grown into a billion dollar industry (Superdata, 2015) that is able to compete with traditional media such as television over viewers (Pires & Simon, 2015). Streamers who gain a big fan base can turn streaming in a full-time job and some streamers gain movie star-like status in the eyes of their followers (Kerr, 2015). The biggest platform to watch streamers is Twitch, who attract 9.7 million daily visitors and more than two million different streamers share their gaming experiences on this platform (Twitch, 2016).
What separates video game streaming from other forms of user-generated content is the level of interactivity. Video-sharing platforms such as YouTube offer content that is pre-recorded and interactivity is mostly a one-way stream from viewer to content creator
(Thelwall, Sud & Vis, 2012). Twitch and similar platforms offer users real-time interaction, which gives the content creator and users unique options that are not found on YouTube. After interviewing both streamers and users, Hamilton, Garretson and Kerne (2014) have concluded that one of the main motivations for using Twitch is the real-time interaction between users and streamer, in addition to being part of an online participatory community. This high level of interaction creates a novelty that is not found in others form of media, merging streamers and spectators in a singular hybrid medium (Taylor, 2016).
The first big streaming platform used for video games was Justin.tv, which started in 2007. Originally intended as a much broader streaming platform, gamers found the platform and started to stream gameplay. In 2011, the owners of Justin.tv launched Twitch, a platform designed for video game streaming and grew into the massive platform it is today (Spangler, 2014). Because no other streaming platform attracts the same amount of viewers, this study will focus on Twitch-users.
4 Study on video game streaming is important because the size and increasing
popularity of this medium suggest that it has solidified its place in the media landscape for the foreseeable future. As traditional media are struggling to retain its audience, Twitch and other similar platforms are still expanding the size of their audience (Taylor, 2016). Because of its recent emergence, very few empirical studies have been done on video game streaming. With so many people spending time on using video game streams, it is important to know who its users are and what drives them to choose this medium over other media.
There are a few scholars who already done studies what motivates users to use stream platforms. Sjöblom & Hamari (2016) have conducted an empirical survey amongst Twitch-users and found that the main motivation for streaming use is tension release, but social integrative and affective motivations are also strongly associated with Twitch-usage. Hamilton et al. (2014) have found in their qualitative study that the community-based approach of streaming sites is appealing for online socialization and the formation of shared identities. Streams are also be used as learning tool to acquire knowledge from eSports professionals on game-related topics. Users watch specific channels on Twitch in order to become better in games they play themselves (Hamari & Sjöblom, 2015). This shows that there are several different motivations to watch videogame streams, some which other more passive mediums are less capable to satisfy. More research into this field is necessary to explain en predict the trends in the current media landscape.
Besides motivations, personality traits are also an important predictor of media-usage and have often been used in past studies (Finn, 1997). Different kind of behaviors on the internet can be predicted by personality traits (Amichai-Hamburger, Wainapel & Fox, 2002). Personality traits are not only a predictor of media-use, but more importantly also an
important predictor of motivations for media-usage (Conway & Rubin, 1991; Jeng & Teng, 2008). Motivations give us a good indication why people use video game streams, but
5 analysis with personality traits reveal deeper lying psychological reasons to choose for certain mediums. By measuring the Big Five personality traits of Twitch-users (McCrea & Costa, 1999), this study aims help mapping characteristics of an average video game stream-user and how personality traits influences the motivations that they have to use video game streams by answering the following research question:
RQ: What is the influence of personality traits on the motivations of Twitch-users to spent
time and money to watch video game streamers?
Theoretical Background
The biggest streaming platform for gamers is Twitch, which attracted an average of 550.000 concurrent viewers per day in 2015. Each users watches with an average of 421 minutes a month. Twitch-users watch significantly more content than an average YouTube-user, who only watches 291 minutes a month (Twitch, 2016). The success of Twitch has led to the emergence of other streaming platform, such as YouTube Gaming, but these are still very small compared to Twitch. An empirical study done on Twitch reveals that most of the streams are being hosted from North America, Europe or East Asia, with the majority coming from the West Coast of the United States (Kaytoue et al., 2012).
Twitch consists of channels that are being run by streamers. Streaming is a form of user-generated content; this means that everybody can freely start a channel. These channels combine live broadcasting of videogame gameplay with a built-in chat room that allows viewers to communicate with the streamers and fellow viewers (Hamilton et al., 2014). Streamers can also broadcast live video footage of themselves simultaneously, but this is optional. Viewers can interact with the streamer through either the chat room or by making a monetary donation with a message attached. These interactions influence the actions of the
6 streamer and the content that the channel produces. Streamers can assign voluntary
moderators who will moderate the chat room, in exchange for certain privileges. receive certain privileges
Streamers can earn revenue through Twitch in two different ways. Twitch offers an option for viewers to subscribe to the streamer for $4.99 a month. Subscribers get certain perks on the channel, such as blocking advertisements, special emoticons or subscriber only broadcasts (Aaron, 2015). The second option to earn money is by donation; users will often donate money with a message for the streamer attached. More often than not will the streamer interact with the donator. Many streamers can earn enough revenue through Twitch each month to convert streaming into a full-time job.
Streaming as a new form of entertainment
The increasing popularity of videogame streams as entertainment seems counter-intuitive when looking at conventional media. Playing videogames has primarily been an active form of entertainment (Grodal, 2000), designed to give gratification to the player. With the emergence of Twitch and other streaming platforms, the importance of the passive spectator has increased massively (Cheung & Huang, 2011). Streaming platforms transform the videogame into a new form of multimedia, with several different uses (Deng, Cuadrado, Tyson & Uhlig, 2015). Gameplay can be seen as the central concept, around the streamer interact with the more passive viewers. Gameplay is not the sole focus of the channel. Interaction between streamer and viewers is an integral part of the motivation to watch streams, found an qualitative study interviewing both viewers and streamers (Hamilton et al., 2014). The content on channels often also include pop-culture themes.. This makes them more accessible and revolves around more than just gaming (Nguyen, 2016). Not
7 audiences to content that are fits with their personal interests (Churchill & Xu, 2016).
Burroughs & Rama (2015) define streaming as a phenomenon that blurs the lines between the real and virtual world by connecting the game space, social networks and face-to-face communication. The real-time interaction adds novelty to the medium, which is very rare and unique compared to other similar media. Similar video sharing sites such as
YouTube are believed to have a higher level of interactivity than traditional media. (Tolson, 2010), but these comments are not answered in real-time, which limits the interactivity.
Motivational factors to watch video game streams
Understanding why humans choose to use certain media-products has always been a key goal of communication science. The uses and gratification approach provides a framework on which research into motivations for media-use can be conducted. This framework can be used to explain why people choose certain media consumption over other types of media
consumption, with factoring in the psychological and social origins (Katz, Blumler & Gurevitch, 1973). The uses and gratification theory (UGT) assumes an active audience who consciously adapts their media-use to their specific needs. According to the theory, there are five different types of needs that users seek to gratify when using media. People use media to gratify the need for new information (cognitive needs), to be gratified on an emotional level (affective needs), to gain and reinforce personal values and opinions (personal integrative needs), to fit in socially and connect with other people (social integrative needs) and to escape reality and relieve stress (tension release needs) (Katz et al., 1973).
These five motivations can be specified for Twitch, as shown by Sjöblom & Hamari (2016). They studied what motivational factors were related to Twitch-usage and adapted the five motivations from the UGT. They found that Twitch-usage correlated most strongly with the tension release need, but cognitive, affective and social integrative were also positively
8 related to Twitch-usage. Personal integrative needs had a negative effect negatively on
Twitch-usage. Hamilton and collegues (2014) have interviewed several streamers and users in their study and found two quite characteristic motivations for using Twitch. Sense of community and social relations with peers were the most important motivations they
identified. This suggests that social integrative motives will also be an important predictor of Twitch-usage in this study.
Hamari & Sjöblom (2015) have examined the motivations of eSports consumers. Many popular streamers on Twitch are also professional eSports athletes and the audience that follows eSports also watches these streams. That is why it would seem plausible that there is a certain carry-over effect from eSports-motivations to video game stream motivations. Hamari & Sjöblom (2015) found two major motivations, acquisition of
knowledge (cognitive needs) and escaping everyday life (tension release needs) that predicted the amount of eSports being consumed. These motivations are also expected to have a
positive influence on Twitch-usage.
Because of the rapidly increasing technological advancements of new media, there are scholars who suggest adding additional motivations when using the UGT as base in
motivational research. Sundar & Limperos (2013) have developed four new gratifications that could be useful when looking at newer types of media. Relationships between user and
medium are more dynamic in comparison to times where mediums were rather static With technology as a source of gratification becoming more and more important in the modern age (Ruggerio, 2000), additional gratifications could find results that would otherwise stay hidden.. Studies into motivational factors of other types of new media such as Pinterest (Wang, Yang, Zheng & Sundar, 2016) and Facebook (Jung & Sundar, 2016) have
implemented these gratifications with success. The four gratifications of Sundar & Limperos (2013) are modality, agency, interactivity and navigability.
9 For the medium Twitch, interactivity is the most interesting gratification to add to the five UGT motivations. Interactivity motivations assume that people seek a certain amount of control of interaction between themselves and the medium. Interactivity is a core aspect of Twitch. Viewers have the power to influence the direction of the content they see in real-time, through messages to the streamer. If the content doesn’t change based on these
interactions, switching to another channel that fits more needs is also an option. It would be interesting to see if these interaction possibilities on Twitch also form a motivation to use the platform. The second motivation that is added is navigability. Navigability refers to the ease on which users can use the platform. While not as interesting as interactivity, positive relations with the usage would indicate that Twitch would also be liked because of the platform and not only because of the streamers. This gratification can be seen as the functionality of the platform.
Modality and agency motivations are not included in this study, because it is less suitable for the medium. Modality refers to the gratification the user receives from the stimuli that send the content. Because Twitch-channels have a lot of different ways to present their content, the results are not that interesting for this study. Agency refers to the gratification that users receive when they can use media to create their own content. Users of modern media are often not only the receiver, but can also become the creator user-generated content and build communities with other people on these media. These gratifications would be interesting if this study was focused on the streamers of Twitch, but was left out because this study focuses on viewers of Twitch.
In sum, this study will aim test seven different motivations to use Twitch. Cognitive, affective, personal integrative, social integrative and tension release motivations are derived from the UGT, while interactivity and navigability motivations were adopted from the new gratifications model of Sundar & Limperos (2013). These study aims to find which
10 motivations are predictors of two aspects of Twitch-usage: time spent watching video game streams and amount of money spent per week. In addition, we will look at the influence of motivations on aspects of the experience Twitch-users have while watching streams. These aspects are enjoyment, engagement and presence.
Personality traits and Twitch-usage
To create better understanding into Twitch-users and their motivations to watch video game streams, the goal of this study is to find out if certain personality traits can be a predictor for these motivations. The field of personality traits consists of two major approaches based on the trait theory (Allport, 1961). The first approach is the Eysenck personality questionnaire that consists of three major traits: extroversion, neuroticism and psychotic (Eysenck, 1965). The second more common used approach is the NEO-personality inventory (Costa & McCrae, 1992), which consists of five personality traits (also known as the Big Five
personality traits). These traits are extraversion (how outgoing and social a person is), agreeableness (how compassionate and cooperative a person is), conscientiousness (how organized and disciplined a person is), neuroticism (how insecure and emotional unstable a person is) and openness to experience (how curious and open to other ideas or forms of art a person is). It is generally believed that these personality traits are innate en primarily
genetically influenced (McCrae et al., 2000). The Big Five Model is widely used to study personality and media consumption, as personality traits are a factor that can influence the online behavior of individuals (McCrea & Costa, 1987). Because of these reasons, this study will base the variables that determine personality traits on the Big Five personality traits. Research into the relationship between personality traits and motivations of media use is a common research theme. Hollenbaugh & Ferris (2014) conducted a study on the different motivational factors of sharing information on Facebook and the Big Five personality traits
11 and found that besides personality traits are not only direct predictors, but by also influencing motivational factors, also becomes an indirect predictor. Seidman (2013) also found that personality traits could be indirect predictors on Facebook-usage through motivations.
Because Twitch has emerged only recently, there is research yet on motivations to use Twitch and personality traits. When looking motivations to play video games and personality traits, it if found that extraversion and agreeableness are as significant predictors for motivations to play online games (Park, Song & Teng, 2011). In the case of the MMORPG World of Warcraft, different motivations have been identified such as online socialization or gaining a sense of accomplishment and extraversion and neuroticism are both strong predictors
(Graham & Gosling, 2013). These studies indicate there personality traits can be predictor for online gaming-related behavior. In this study we aim to find out which personality traits can predict motivations to use Twitch.
Besides the influence of personality traits on motivation, it is also interesting to determine if personality traits has a direct effect on the two different aspects of Twitch-usage: hours spent per week and money spent per week. While no studies have looked at personality traits as predictor for Twitch-usage of video game stream-usage, there are studies done on overlapping forms of media. Because general topics of Twitch-streams are related to video games, it could be argued that the relationship between personality traits and video game-usage has some overlap on the relationship between personality traits and Twitch game-usage. longitudinal study found that youth who scored higher on openness spent more time playing videogames and spending time on the computer (Witt, Massmann & Jackson., 2010), while players with a higher agreeableness score are more likely to play Massive Multiplayer Online Role-Playing Games (Collins, Freeman & Chamarro-Premuzic, 2012). This is also supported in a study that compared non-players to online gamers. Online gamers scored higher on openness, contentiousness and extraversion (Teng, 2008). These studies indicate that
12 personality traits can predict media-usage and it would be plausible that some personality traits can have direct influence on Twitch-usage, aside from the indirect influence they have through motivations. With all these topic in mind we expect that personality traits influence Twitch-usage directly and indirectly through motivations, as shown in Figure 1.
Figure 1. Conceptual model
Methods
Selection of research units
In order to gain enough data to answer the research question, a survey was conducted spread out amongst Twitch-users. Survey research was deemed as the most effective method for this study because the variables needed to answer the research question can be measured with a survey and because of the ability to gain many respondents in a relative short amount of time.
Personality Traits Extraversion Agreeableness Conscientiousness Neuroticism Openness Motivations Cognitive Affective Personal Integrative Social Integrative Tension Release Interactivity Navigation Twitch-usage Hours watching streamers Dollars spent on streamers
13 Because Twitch is an online medium, the easiest way to find respondents was to make an online survey. The target population are Twitch-users that watch video game streams on a regular basis. Because Twitch is by far the most used platform to watch video game streams, it was decided that only Twitch-users would be able to participate, as this made the
questionnaire shorter and this excludes only a few individuals who watch video game streams on a different platform.
Participants were recruited on the social news site Reddit, which has a popular section where users can discuss and post about Twitch. Members of the site were not approached individually, but with a general message on the main page. It was indicated that the data would only be used for a master’s thesis and not for commercial purposes. As an incentive to participate in the research, a raffle was held in which five participants could win a $20 gift card. All participants were self-selected. The online questionnaire was released on the 19th of December and participants could participate until the 6th of January. The target sample size was at least 100 participants to avoid validity issues with the results.
A total of 1002 participants started the questionnaire, but only 635 participants were left after removing all incomplete data. Additionally, data from five participants who
indicated that they spent 0 hours per week watching streamers on Twitch were excluded from all analyses. The final dataset consisted of 630 participants ranging in age from 11 to 59 years (M = 22.7, SD = 5.46) with 89% being male.
Operationalization of variables
Several dependable variables were measured in the questionnaire. Hours per week spent watching streamers was measured on a self-reported scale asking how much time per week they spent watching streamers on Twitch. Money spent on Twitch was self-reported average the participant spent per week on the platform. The valuta is United States dollar as this is the standard valuta of Twitch. There were also three dependable variables measuring various
14 aspects of experience that Twitch-users could have when watching streams. These variables were enjoyment, engagement and presence.. Participants were asked to rate on a scale ranging from 1 – 7 how much the statement applied to them. A complete list of items for all the scales used in this study can be found in Appendix A.
Enjoyment was measured taking items from Feng, Chan, Brzezinski & Nair (2010) study about measuring enjoyment in playing videogames.. An example item used for this scale is ‘I feel happy when I’m using Twitch’. Engagement was measured to determine how immersive the medium is and based on the focus attention-scale (Wiebe, Lamb, Hardy & Sharek, 2014). An example item used for this scale is ‘When I’m using Twitch, times slips
away’. Presence was measured to determine how much people belief they are in the
generated environment and items were taken from the ITC-Sense of Presence Inventory (Lessiter, Freeman, Keogh & Davidoff , 2001). An example item for this scale is ‘I have the
best viewpoints of the action when I’m using Twitch’. The enjoyment scale consisted of 3
items (.89), the engangement scale also consisted of 3 items (.85), and the presence scale consisted of 4 items .65) (see Appendix A for all items).
Five gratifications from the UGT-theory (Katz et al., 1973) and two from the new gratifications model (Sundar & Limperos, 2013) are being measured on a 7-point likert scale, ranging from strongly disagree to strongly agree. The five motivational factors that are derived from the UGT-theory are cognitive needs, affective needs, tension release needs, social integrative needs and personal integrative needs.
Cognitive needs are comprised of two subscales, Game information and Learning
from professionals. To measure these subscales, three items from the Information
Seeking-scale (Papacharissi & Rubin, 2000) were used for the Game information-Seeking-scale. Three items
were adapted to fit the medium Twitch. For the Learning from professionals-scale, three items from the Cognitive need-scale (Lim, 2009) were used. An example item for cognitive
15 needs is ‘I use Twitch to learn about new games’. The items for affective needs were based on the Character engagement-scale (Bartsch, 2012). The items were changed in order to make the participants reflect on the streamers instead of video game characters. An example item to measure affective needs is ‘I use Twitch because I identify with certain streamers’. Tension release needs consists of three subscales, Entertainment (adapted from the Video
Game Arousal-scale (Sherry et al., 2006)) Passing Time (adapted from the Pass Time-scale
(Papacharissi & Rubin, 2000)) and Escapism (adapted from the Negative escapism-scale (Hagstrom & Kaldo, 2014)). An example item for measuring tension release needs is ‘I use
Twitch because it keeps me on the edge of my seat’. To measure personal integrative needs,
two subscales were used, personal development-scale and opinion of peers-scale. The
personal development-scale is made out of three items that related to personal integrative
needs on the Interpersonal utility-scale from Papacharissi & Rubin (2000). Opinion of
peers-scale is based on the cool and new trend-peers-scale from Smock, Ellison, Lampe and Wohn
(2011). An example item for measuring personal integrative needs is ‘I use Twitch to express
myself freely’. Social integrative needs is comprised of two subscales; the companionship-scale (Smock et al., 2011) and the community building-companionship-scale (Sundar & Limperos, 2013). An
example item for measuring social integrative needs is ‘I use Twitch because it makes me feel
less lonely’. All items that are used for these scales can be found in Appendix A.
The two gratifications from Sundar & Limperos (2013) are interactivity needs and
navigability needs and focus more on using the platform itself as a tool of gratification, instead on the content that is shown on this platform. The interactivity and navigation motivations are based on the new gratifications MAIN model (Sundar & Limperos, 2013). They introduced the activity-scale and the dynamic control-scale that are used for measuring interactivity needs and the navigation-scale that is used for measuring navigation needs. An
16 example item is ‘I use Twitch because it is fun to explore the platform’ as shown in Table 1, all scales and subscales for motivations tested reliable using the Cronbach’s alpha.
Table 1
Reliability of motivation scales
Motivations Subscale M SD Cronbach's α
Cognitive motivations 4.63 1.31 .84 Game Information 4.72 1.51 .86 Learning from professionals 4.53 1.61 .85 Affective motivations 4.77 1.43 .79 Tension Release motivations 4.58 .98 .81 Entertainment 5.23 1.09 .71 Passing Time 5.58 1.17 .82 Escapism 2.93 1.73 .92 Personal Integrative motivations 2.92 1.07 .78 Personal Development 4.42 1.64 .81 Opinion of Peers 2.17 1.13 .81 Social Integrative motivations 3.64 1.38 .84 Companionship 2.99 1.68 .89 Community Building 4.30 1.64 .84 Interactivity motivations 3.82 1.49 .88 Activity 4.36 1.61 .72 Dynamic Control 3.48 1.65 .91 Navigability motivations 4.59 1.40 .82
Personality traits are measured through the Big Five Inventory (BFI) (John et al., 1991), containing 44 items that the respondents answer about themselves in order to measure the Big Five personality traits. The five traits that are measured in this index are extraversion,
17
agreeableness, conscientiousness, neuroticism and openness to experience. The main reason
why this index was chosen was because of the relative low amount of items and short times it takes to complete. The NEO-Five Factor index (McCrae & Costa, 2004) contains 240 items, while the BFI only contains 44 items. Each items consists of a short statement, for instance ‘I
see myself as someone who is talkative’ on which the respondents answer in what matter
these statements apply to themselves. The items are answered on a 1 to 5 likert-scale, ranging from ‘Disagree Strongly’ to ‘Agree Strongly’. A study from Thalmayer, Saucier and
Eigenhuis (2011) compared the validity of the BFI to other indexes, which measure the Big Five personality traits, and the BFI was deemed a valid and reliable index. This result is supported by a study from Gosling, Rentfrow and Swann (2003), who retested the reliability of the BFI, with scores between .76 and .83. This is also supported by our own reliability tests, as shown in Table 2.
Table 2
Reliability of personality scales
Personality Traits M SD Cronbach's α
Extraversion 2.85 .76 .85 Agreeableness 3.54 .59 .75 Conscientiousness 3.19 .62 .79 Neuroticism 2.92 .73 .80 Openness 3.68 .50 .71 Results
In order to answer whether personality traits can be predictors for motivations to use Twitch, a correlation analysis between personality traits and motivations was performed Several interesting relationships between personality traits and motivational factors were found in the
18 correlation analysis, as shown in Table 3. The correlation analysis shows that neuroticism positively correlates with tension release, social integrative, affective and personal integrative motivations. Openness correlates positively with affective, personal integrative, social
integrative, interactivity and navigability motivations, but negative with tension release motivations. Conscientiousness seems to correlate negatively with social integrative and tension release motivations. Extraversion correlates positive with cognitive, personal integrative, tension release motivations, interactivity motivations and navigability
motivations. Agreeableness shows a significant positive relationship with cognitive, affective, social integrative, interactivity and navigability motivations. There seem to be enough
indications that there are significant relationships between personality traits and motivations to perform analysis to explain these correlations.
In order to explain which personality traits predict which motivations to use Twitch, several regression analyses between personality traits and motivations were performed, as shown in Table 4. Neuroticism seems to be the most influential personality trait with significant effects on every motivation except cognitive motivations. Extraversion and agreeableness are also significant predictors for a number of motivations. Openness only is only a significant predictor for interactivity motivations. Social integrative and tension release motivations are the motivations that are best predicted by personality traits, this effect is still weak however
19
Table 3
Correlations between personality traits, motivations and Twitch-usage
Measure EX AG CO NE OP Time Money
Cognitive .10* .12** .05 .01 .03 .08* .02 Affective .04 .18** -.04 .13** .12** .15** .22** Personal .18** .05 -.04 .10* .14** -.03 .17** Social -.01 .10* -.10* .25** .11** .14** .24** Tension .20** -.03 -.20** .31** -.10** .24** .17** Interaction .16** .12** .01 .05 .21** .02 .23** Navigability .16** .20** .04 .02 .16** -.04 .14 Time -.15** .02 -.03 .07 -.11** --- --- Money .12** .11** .06 -.01 .10* --- ---
Note. EX = Extraversion, AG = Agreeableness, CO = Conscientiousness,
NE = Neuroticism, OP = Openness. * p <.05. ** p <.01.
Table 4
Multiple regression analyses of personality traits as predictor for motivations Motivations to watch streamers on Twitch
Predictor CM b* AM b* PIM b* SIM b* TRM b* IM b* NM b* Constant 2.83*** 1.04 .53 -.33 4.09*** -.19 .83 Extraversion .13** .08 .24*** .08 -.08 .16*** .17*** Agreeableness .13** .21*** .06 .15*** .08* .10* .20*** Conscientiousness .03 -.05 -.06 -.06 -.08 -.03 -.03 Neuroticism Openness .09 -.05 .20*** .07 .19*** .07 .31*** .10* .26*** -.05 .14** .15** .13** .07 R2 .03 .08 .08 .11 .12 .08 .08 F 3.81** 10.51*** 10.36*** 15.16*** 16.32*** 10.14*** 10.84***
Note. CM = Cognitive Motivations, AM = Affective Motivations, PIM = Personal Integrative Motivations,
SIM = Social Integrative Motivations, TRM = Tension Release Motivations, IM = Interactivity Motivations, NM = Navigability motivations. * p <.05. ** p <.01. *** p <.001.
20 Next we examined the relationship between motivations and Twitch-usage.
Correlation analysis between motivations and Twitch-usage, as shown in Table 3, reveal interesting relationships between motivations and time spent watching streamers and between motivations and money spent on Twitch . The regression analysis with time spent watching streamers as dependable variable is significant, as shown in Table 5. Ten percent of the variation in time can be predicted by the motivational factors. Tension release and social integrative motivations have a significant weak association, while personal integrative and navigability motivations have a significant weak negative association. The regression model with money spent on Twitch as dependable variable is significant, as shown in Table 5. Nine percent of the variation in money can be predicted by the motivational factors. Affective, social integrative and interactivity motivations have a significant weak association with money spent on Twitch.
Next we examined personality traits as a direct predictor for both aspects of Twitch-usage. Correlation analysis, as shown in Table 3, reveal that there are indications for this effect Extraversion and openness correlate significantly with time and money spent on Twitch. Additionally agreeableness also correlates significantly with money spent on Twitch. The regression model with hours spent watching streamers on Twitch as dependable variable, as shown in Table 6, is significant. Only three percent of the variation in hours can be
predicted by personality traits. Extraversion is the only personality trait with a significant, weak association. The regression model with money spent on Twitch as dependable variable, as shown in Table 6, is significant. Three percent of the variation in dollars can be predicted on the basis of personality traits. Extraversion and agreeableness have a significant weak association.
21
Table 5
Regression analyses of motivations as a predictor for hours watching streamers and spending behavior on Twitch
Predictor Time spent Money spent
b* b* Constant 1.83*** .60 CM .08* -.06 AM .09 .13** PIM -.16** -.01 SIM TRM .14* .21*** .11* .05 IM .03 .12* NM -.17** -.01 R2 .10 .09 F 9.96*** 23.61***
Note. CM = Cognitive Motivations, AM = Affective Motivations, PIM = Personal Integrative Motivations, SIM = Social
Integrative Motivations, TRM = Tension Release Motivations, IM = Interactivity Motivations, NM = Navigability motivations. * p <.05. ** p <.01. *** p <.001.
Table 6
Regression analyses of personality traits as a predictor for hours watching streamers and spending behavior on Twitch
Predictor Time spent b* Money spent b* Constant 3.90*** -.69 Extraversion -.12** .12** Agreeableness .06 .10* Conscientiousness .02 .03 Neuroticism Openness .04 -.08 .08 .03 R2 .03 .03 F 3.71** 3.79** Note. * p <.05. ** p <.01. *** p <.001.
22 Finally this study wants to find out how these motivations influence the way Twitch is
experienced. Regression analyses were performed to see the effects of motivations.. Enjoyment is significantly associated with affective motivations, but has also significant negative associations with personal integrative motivations, social integrative motivations and tension release motivations. Engagement and presence are both positively associated with social integrative, tension release and navigability motivations.
Table 7
Multiple regression analyses with motivation as predictor of enjoyment, engagement and presence
Predictor ENJ b* ENG b* PRE b* Constant 6.98*** -.43 .20 Cognitive Motivation .01 -.05 .05 Affective Motivation .11* .01 .05 Personal Integrative -.28*** .07 .06 Social Integrative -.13* .22*** .16*** Tension Release -.09* .36*** .28*** Interactivity .07 .01 .05 Navigability .08 .09* .24*** R2 .10 .32 .40 F 9.79*** 42.14*** 59.82***
Note. ENJ = Enjoyment,, ENG = Engagement
PRE = Presence. * p <.05. ** p <.01. *** p <.001
Conclusion and discussion
This study wanted to explore the influence of personality traits on motivations to watch streamers on Twitch. Personality traits are a significant predictor for these motivations. Neuroticism emerged as the strongest predictors, as this trait was of influence to almost all
23 motivational factors. While Twitch-users score low on neuroticism in comparison to other personality traits like agreeableness, users who score higher have strong motivations to use the platform. The relationship with tension release seems logical, social integrative motives are an interesting relationship. It could be that person who score higher on neuroticism can find more social connection on Twitch.. This does raise the question if Twitch might have a positive effect in the lives of these higher neurotic persons. Follow-up research into this topic could be interesting.. Extraversion and agreeableness were also identified as personality traits that predict motivation. A higher level of extraversion is related to more personal integrative motivation and more cognitive motivation. More extravert users are more likely to use Twitch because for self-validation or to learn new information. Twitch-users with higher agreeableness are more likely to watch because of affective motivations or social integrative motives. This makes sense, a higher kindness increases to the motivation to be interested in the life of the streamer and to socialize with other Twitch-users.
This study also examined influence of different motivational factor on two aspects of Twitch-usage, time spent and amount of money spent. Tension release emerged as the
strongest predictor for hours spent watching streams. Social Integrative motives is also strong predictor, while personal integrative motives have a negative association with the amount of time spent on video game streams. These results fall in line with the study of Sjoblöm & Hamari (2016), and suggest that the strongest motivations to spend more hours on Twitch is to escape stress and socializing with other users or streamers. This understates the
importance as gaming as a social platform. Gaming seems become less individualistic compared to the past and Twitch shows one way to integrate social aspects into gaming. Gamedevelopers should evaluate how to improve social aspects of their game if they want to fill this need which appeared throughout this study.. As neuroticism was revealed as a strong predictor for both tension release and social integrative motivations, more insecure and tense
24 use Twitch-longer to relieve stress and fulfill social needs.
The negative association with personal integrative needs suggest use Twitch is less used as a medium to validate their status and beliefs. This could be because Twitch might be seen as a strange hobby , so users aren’t that open about it. If Twitch is still seen as weird or uncool by the general public, Twitch and other streamingplatforms could increase market themselves towards a bigger audience.. Interactivity motivations have a small positive effect on money spent. This could be explained by the fact that donating money on Twitch is an feature which leads to interaction with streamers. It is plausible that people who want higher levels of interaction form their medium, would spent more money. Other media should investigate the possibility of increasing the interaction possibilities to create extra revenue. Navigability motivations seem to have a negative relationship with the amount of time spent on Twitch. This could be interpreted as Twitch-users either don’t care whether about the interface of Twitch, or that people generally don’t navigate the platform as much when they found a stream.
When looking at how users experience watching streams, personal integrative motivations have a negative relationship with enjoyment, meaning that users who care less about using Twitch to gain or maintain status are more likely to enjoy their time on Twitch. Both engagement and presence seem to have a strong relationship with social integrative and tension release motives. Users who want use Twitch for socializing or to escape reality. are more immersed into the medium and the content.
A few limitations of this study must also be discussed. While survey is an much-used method to study personality traits and motivations, it a limited method to prove causality. An experimental design or an longitudinal study would be better suited to measure causality. This would also improve the reliability of the study, as hours and money spent are
25 E-Sports is another segment on Twitch that deserves attention from the scientific community, but it was not possible to include in this study. This study was also focused on motivations of video game streams, but these do not explain how users experience video game streams and lacks an assessment of sustainability for the future of this medium. Because Twitch is such a new medium, current scientific knowledge on this topic is still limited. Qualitative studies on the gratifications, experiences and place video game streaming has in the life of these users could provide a more complete picture in understanding new media.
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33 Appendix A
Scales and items Cognitive motivations
Knowledge on games (Papacharissi & Rubin, 2000) - I use Twitch to learn about new games
- I use Twitch because it is an easy way to learn about games - I use Twitch because the information about games is free
Learning from professionals (Lim, 2009)
- I use Twitch to learn from professionals gamers
- I use Twitch to learn a game trick something I am not familiar with - I use Twitch to get better in a specific videogame
Affective motivations
Emotional engagement (Bartsch, 2012)
- I use Twitch because I identify with certain streamers
- I use Twitch because I like to sympathize with certain streamers - I use Twitch because I live through certain streamers experiences
Tension Release motivations Entertainment (Sherry et al, 2006) - I use Twitch because it excites me
- I use Twitch because it keeps me on the edge of my seat - I use Twitch to feel entertained
Pass Time (Papacharissi & Rubin, 2000) - I use Twitch to pass the time when I’m bored - I use Twitch when I have nothing better to do - I use Twitch to occupy my time
Escapism (Hagstrom & Kaldo, 2014)
- I use Twitch to avoid thinking about real-life problems - I use Twitch to avoid real-life social situations
- I use Twitch so that I don’t have to deal with everyday problems
Personal Integrative Motivations
Personal development (Papacharissi & Rubin, 2000) - I use Twitch to express myself freely
- I use Twitch because I enjoy participating in discussions - I use Twitch because I wonder what other people think of me
34 Opinion of peers (Smock et al., 2011)
- I use Twitch because everybody else is doing it - I use Twitch because it is the cool thing to do
- I use Twitch because other people will think I’m cool Social Integrative Motivations
Companionship (Smock et al., 2011) - I use Twitch so I won’t have to be alone
- I use Twitch when there is no one else to talk to - I use Twitch because it makes me feel less lonely
Community-building (Sundar & Limperos, 2013) - I use Twitch to connect with like-minded people - I use Twitch to expland my social network
- I use Twitch because it makes me realize that I am part of a community
Interactivity
Activity (Sundar & Limperos, 2013)
- I use Twitch because it is not a passive interaction
- I use Twitch because I can do a lot of different things on Twitch
Dynamic Control (Sundar & Limperos, 2013) - I use Twitch because it gives me control
- I use Twitch because it allows me to be in charge
- I use Twitch because I am able to influence to control my interactions with the interface
Navigability
Navigation (Sundar & Limperos, 2013) - I use Twitch because it is easy to use
- I use Twitch because the interface offers visual aids for more effective use - I use Twitch because it is fun to explore the platform
Enjoyment (Fang et al., 2010) -I feel unhappy when using Twitch. -I feel miserable when using Twitch. -I feel worried when using Twitch
*Note Reversed Items
Engagement (Wiebe et al., 2014)
-When I am on Twitch, I often lose track of the world around me. -I block out things around me when I am using Twitch
35 Presence (Lessiter et al., 2001)
-It is easy to forget that I am watching a screen when using Twitch.
-When I’m on Twitch, I often want to see more of the space in the displayed environment than I am able to
-I have the best viewpoints of the action when using Twitch - Time slips away when I’m using Twitch
Personality traits
Items and matching personality traits
Extraversion: 1, 6R, 11, 16, 21R, 26, 31R, 36 Agreeableness: 2R, 7, 12R, 17, 22, 27R, 32, 37R, 42 Conscientiousness: 3, 8R, 13, 18R, 23R, 28, 33, 38, 43R Neuroticism: 4, 9R, 14, 19, 24R, 29, 34R, 39 Openness: 5, 10, 15, 20, 25, 30, 35R, 40, 41R, 44 R = reversed item
I see myself as someone who... 1. Is talkative
2.Tends to criticize others 3. Does a thorough job 4. Is depressed
5. Is original 6. Is reserved 7. Is helpful
8. Can be somewhat careless 9. Handles stress well
10. Is curious about many different things 11. Is full of energy
12. Starts arguments with others 13. Is reliable
14. Can be tense 15. Is a deep thinker
16. Generates a lot of enthusiasm 17. Has a forgiving nature
18. Tends to be disorganized 19. Worries a lot
20. Has an active imagination 21. Tends to be quiet
22. Is generally trusting 23. Tends to be lazy 24. Not easily upset 25. Is inventive
36 27. Can be col
28. Finishes my tasks 29. Can be moody
30. Values artistic experiences 31. Is sometimes shy
32. Is considerate and kind to almost everyone 33. Does things efficiently
34. Remains calm in tense situations 35. Prefers work that is routine 36. Is outgoing
37. Is sometimes rude to others
38. Makes plans and follows through with them 39. Gets nervous easily
40. Likes to reflect, play with ideas 41. Has few artistic interests 42. Likes to cooperate with others 43. Is easily distracted
