A foray into researching: all about Twitch users

Introduction

This research paper explores the psychological and social motivations for using the amazingly popular social media/live streaming platform Twitch by conducting a small survey of users.

Keywords:  Live Streaming, Twitch, Social Identity Theory, Uses and Gratifications Theory, Psychological motivations

Psychological Motivations for Twitch users

This research paper explores the psychological and social motivations for using the amazingly popular social media/live streaming platform Twitch. This small survey of 87 participants could not establish a relationship between psychological motivations (information seeking, entertainment or social) and watching continuance intention. However, it did support the hypothesis and align with past research that those users who have online social identities aligned to the broadcaster do use the platform for information seeking and entertainment, and those who align their identities with groups of other audience members use Twitch for social motivations.  A novel finding of this research was that whether or not people identified with Group or Broadcasters, they all experience para-social feelings towards the Broadcaster, and also a sense of community with other members, are driven by conformity motivations in their use of the platform, and share a co-experience of Twitch with other users in their group also.

Background

Twitch Live Streaming Platform

Twitch.tv is a live streaming platform where broadcasters stream content on their channel, mostly streams of themselves playing video games (Ewalt, 2013). Twitch is also home to official broadcasts of esports tournaments, and more recently hosting broadcasters of “real life” content (Wikipedia, 2019). Viewers can subscribe to broadcasters’ channels, to watch their live streams, and interact with other viewers and the broadcaster (when they read the messages) via stream chat. Some live streams have over 20,000 concurrent viewers, and the chat messages can stream past at what appears an unintelligible speed to a novice. Twitch audience have their own ways of playing around with chat in the extra-large live streams (greater than 10,000 concurrent viewers), such as using ASCII and copypasta art, in a style called crowdspeak (Ford, Gardner, Horgan, & Liu, 2017). Twitch chat is “simultaneously incoherent and enjoyable” page 5 (Ford et al., 2017). Through combining broadcast and the somewhat incoherent chat, Twitch is a new and unique form of participatory social media (Hu, Zhang, & Wang, 2017; Jenkins, 2006).

Since being spun off from its parent site, justin.tv, in 2011 (Ewalt, 2013; Ford et al., 2017), Twitch.tv’s popularity has continued to grow astronomically . According to Twitch’s own website, they have upwards of 1.3m concurrent viewers at any given moment, over 3m creators streaming monthly, and more than 15 m average daily visitors (Twitch, 2019). Half a trillion minutes were streaming in 2018 (Twitch, 2019), and in 2014 they were the 4th largest streaming site in the US (Ford et al., 2017; Hilvert-Bruce, Neill, Sjöblom, & Hamari, 2018). Clearly, Twitch is meeting a very prevalent need in society.

All of this is very bewildering to new users, or students of communication media who are unfamiliar with the platform. It prompts the question: why do people consume different types of media (Hilvert-Bruce et al., 2018). The purpose of this paper is to hypothesize why people use Twitch and test the hypotheses.

Literature Review and hypothesis development

The theoretical background for this research paper is grounded in two theories related to computer mediated communication: uses and gratification theory and social identity theory. The relationships between the theory, concepts and hypotheses are illustrated in Figure 1.

Figure 1 Conceptual Model for Research Paper

Social identity theory and social identification concept

In their study of intergroup conflict Tajfel and Turner proposed social identity theory, where people hold multiple social identities along with their individual one (Tajfel & Turner, 1979). These social identities form where we experience a sense of oneness and belonging to a community, and this can happen online (Hu et al., 2017; Xiao, Li, Cao, & Tang, 2012). Individuals seek to create online social identities (even when otherwise anonymous) and these identities help foster trust and information and social exchange between community members (Postmes, Spears, & Lea, 1998; WALTHER, 1996; Xiao et al., 2012). This social identification concept and forming of online social identities leads to continuous use intention(Chang & Zhu, 2011; Hu et al., 2017). For owners of online sites such as Twitch, continuous use intention is a key objective of the site.

Uses and Gratification Theory

Uses and Gratification Theory (UGT) attempts to answer the question about why people choose to consume different types of media (Hilvert-Bruce et al., 2018). According to UGT, media engagement behaviors are aimed at “the fulfilment of individual psychological needs” page 59 (Hilvert-Bruce et al., 2018).

Social motivators of media engagement behavior include information seeking, entertainment, and social motivations such as meeting new people, social interactions and support, sense of community, social anxiety and external support, however research has found social anxiety and external support were not supported as uses for Twitch (Hilvert-Bruce et al., 2018). 

Information Seeking and Entertainment

A number of papers have information seeking and knowledge exchange/ sharing as a reason for use of social media platforms and online forums (Chiu, Hsu, & Wang, 2006; Ford et al., 2017; Hilvert-Bruce et al., 2018; Pendry & Salvatore, 2015; Xiao et al., 2012). Entertainment is also a psychological motivator in the use of social networking sites (Chang & Zhu, 2011).

Information seeking and entertainment are important motivators for using Twitch, because audiences can learn how to play games while enjoying watching the most experienced players in the world, either during tournaments or on their live stream channel (Ewalt, 2013; Hilvert-Bruce et al., 2018).

H1.1 Use of Twitch for information seeking and entertainment motivations are positively correlated with intention of continuation of engagement.

Social Motivations

Meeting new people, social interactions and sense of community are noted in research as important psychological reasons for using social networking sites (Chang & Zhu, 2011) and live streaming sites (Hilvert-Bruce et al., 2018).  A sense of community online involves an individual experiencing feelings of belonging, having a say, fulfilment of needs, feeling a bond with others, and mutual influence between members (Hilvert-Bruce et al., 2018; Mcmillan & Chavis, 1986; Peterson, Speer, & Mcmillan, 2008). Online social ties form between members’ online social identities from the social interactions and sense of community they have, and further reinforce online social identity and social identification concept outlined in the previous section (Hilvert-Bruce et al., 2018; Xiao et al., 2012).

H1.2 Use of Twitch for social motivations are positively correlated with continuous watching intention.

Types and Antecedents of Social Identification

Further to social identity theory, social identification concept and UGT, research identifies two types of social identification for users of live streaming platforms: broadcaster identification and group identification (Choe, 2019; Hu et al., 2017).

Identification with the Broadcaster is motivated by individual identification in the classical sense: wanting to be like someone you admire (Hu et al., 2017). Broadcaster identification on live streaming platforms like Twitch is caused through the effects of para-social activity, where the audience has the illusion of an individual  relationship with the broadcaster facilitated by the stream chat and the responses of the broadcaster to individuals requests (through techniques like footing and recruitment as explained by Choe, 2019 and Hu et al, 2017).  

H3.1 Para-social experience is positively corelated to Broadcaster Identification through Twitch

This paper hypothesizes that along with para-social experience, audience follow certain broadcasts because they want to learn how they play video games (information) or because they enjoy their streams for entertainment reasons.

H2.1 Use of Twitch for information seeking and entertainment motivations is positively correlated with Broadcaster Identification.

Identification with a group is the sense of community (belongingness and oneness) generated through online social ties and social interactions that occur between online social identities (Hilvert-Bruce et al., 2018; Hu et al., 2017). Group identification occurs through the social interaction with other audience members facilitated through stream chat and also offline (Choe, 2019; Hu et al., 2017) and is caused by the social effects outlined above in UGT section.  It can be measured in terms of co-experience, where interaction between members co-creates the community, through cognitive communion, resonant contagion and sense of community (Hilvert-Bruce et al., 2018; Hu et al., 2017).

Of course, communities form in real life (offline) too, and these groups can influence social interaction and sense of community occurring online and also be influenced by it (Jenkins, 2006). Social media enhanced real-time streaming video sites can reduce the physical distance between friends (Lim, Cha, Park, Lee, & Kim, 2012), they can encourage civic activity offline (Pendry & Salvatore, 2015) and generate a virtuous feedback cycle in participatory media (Jenkins, 2006).  Chang and Zhu discuss that having a critical mass of friends on social media sites can encourage others to join them (Chang & Zhu, 2011), and therefore this conformity motivation could be another psychological motivation for use of new social networking/live streaming services like Twitch.

H2.2 Use of Twitch for social motivations is positively correlated with Group Identification.

H3.2 Conformity Motivation, Sense of Community and Co-experience is positively correlated to Group Identification

Method

Participants.

87 participants responded to the survey. The survey was administered over two 24-hour periods on the Amazon MTurk platform. The first batch yielded 52 responses and the second batch 37 responses. The second batch was performed in order to have a large enough sample for regression analysis (although this wasn’t successful). Survey participation was voluntary, and survey participants each received between USD$0.70 and $0.85 compensation via the MTurk platform.

80% of the survey participants identified as male and the remaining 20% as female. 72% of participants were between 25-34 years old, 14% were 35-44, 9% 18-24 and 5% were 45-54 years old. The participants were in either the USA or India as shown in Figure 2 below. The size of the circle indicates the quantity of responses from that location.

Figure 2 Location of participants

Present Survey

The survey consists of 38 questions obtained from past studies outlined in the Literature Review section, whose responses were examined for pairwise positive correlation to prove the hypotheses. See Table 1 for a summary of the areas of the survey and the number of questions.

Scale

All responses are measured using a 7-point Likert Scale: Strongly Disagree, Disagree, Somewhat Disagree, Neither agree nor disagree, Somewhat Agree, Agree, Strongly Agree.

Engagement measures

This paper measures engagement with Twitch using continuous watching intention (Chang & Zhu, 2011; Hu et al., 2017; Kang, Hong, & Lee, 2009). Although there are other measures such as self-reported frequency, psychological and financial, intention has been chosen as the right balance between easy to measure and yet less subjective (Hilvert-Bruce et al., 2018). 

Information seeking and entertainment motivation measures

Three questions explore information seeking motivations and two questions measure entertainment motivation (Chang & Zhu, 2011; Hilvert-Bruce et al., 2018) for Hypotheses H1.1 and H2.1.

Social motivation measures

Social motivations with reference to Hypotheses H1.2 and H2.2 are measured by multiple questions. One question to determine if Twitch is used for Meeting New People (Chang & Zhu, 2011; Hilvert-Bruce et al., 2018), six questions that explore the nature of participant’s online social identities, whether they know others’ or others know their screen name, real name or personality (Postmes, Spears, & Lea, 1998; WALTHER, 1996; Xiao, Li, Cao, & Tang, 2012), and one question to measure Online Social Ties by asking about frequency of communication with other audience members (Xiao et al., 2012).

Group Identification and Broadcaster Identification measures

For Hypotheses H2.1, H2.2, H3.1 and H3.2 Group identification is measured by 2 questions which are two different types of group identification: identification with other audience members and feeling like being in a club with other fans of the broadcaster (Hu et al., 2017; Masayuki Yoshida, Bob Heere, & Brian Gordon, 2015). Broadcaster identification is measured by 4 questions about whether people use Twitch to follow a broadcaster and whether they see them as a model to follow, align with their values or are proud to follow them (Hu, Zhang, & Wang, 2017; Liu, Liao, & Wei, 2015; Shamir, Zakay, Breinin, & Popper, 1998).

Antecedent measures

For Hypotheses H3.1 and H3.2, para-social experience is measured through three questions regarding recruitment and reactions between individual and broadcaster (Hartmann & Goldhoorn, 2011; Hu et al., 2017). Conformity motivation is measured by asking two questions to see whether people the respondent communicates with are also watching Twitch (Chang & Zhu, 2011). Co-experience is measured by one question regarding cognitive communion (sharing thoughts with other members) and two questions regarding resonant contagion (mutual influence on behavior of audience) (Hu et al., 2017; Lim, Cha, Park, Lee, & Kim, 2012). Sense of community is measured using five questions to determine belongingness, needs fulfilment and other indicators (Hilvert-Bruce, Neill, Sjöblom, & Hamari, 2018; Mcmillan & Chavis, 1986; Peterson, Speer, & Mcmillan, 2008).

Support Threshold

Given the results are to be analyzed as correlation of pairwise relationships, support will be calculated by taking the proportion of correlated relationships over total relationships. A hypothesis will be supported if the support is greater than 70%.

AreaFinalReference
Brief Sense of Community5(Hilvert-Bruce, Neill, Sjöblom, & Hamari, 2018; Mcmillan & Chavis, 1986; Peterson, Speer, & Mcmillan, 2008)
Broadcaster Identification4(Hu, Zhang, & Wang, 2017; Liu, Liao, & Wei, 2015; Shamir, Zakay, Breinin, & Popper, 1998)
Channel Size Preference1(Hilvert-Bruce et al., 2018)
Cognitive Community1(Hu et al., 2017; Lim, Cha, Park, Lee, & Kim, 2012)
Conformity motivation2(Chang & Zhu, 2011)
Continuous watching intention (CWI)2(Chang & Zhu, 2011; Hu et al., 2017; Kang, Hong, & Lee, 2009)
Engagement Indicator1(Hilvert-Bruce et al., 2018)
Entertainment motivation2(Chang & Zhu, 2011; Hilvert-Bruce et al., 2018)
Experience of parasocial interaction3(Hartmann & Goldhoorn, 2011; Hu et al., 2017)
Group identification2(Hu et al., 2017; Masayuki Yoshida, Bob Heere, & Brian Gordon, 2015)
Information motivation3(Chang & Zhu, 2011; Hilvert-Bruce et al., 2018)
Meeting new people1(Chang & Zhu, 2011)
Online Social Identity6(Postmes, Spears, & Lea, 1998; WALTHER, 1996; Xiao, Li, Cao, & Tang, 2012)
Online Social Tie1 
Resonant contagion2(Hu et al., 2017; Lim et al., 2012)
General2 
Total38 
Table 1 Areas for Questions

Findings

Results

Question responses were compared pairwise in this study. There were 38 questions and the results of positive correlations are shown in Tables 2 and 3. There were no negative correlations.

The results for each pairwise comparison is noted in the tables 2 and 3 below, for each hypothesis. The results of the hypotheses involving group identification (H2.2 and H3.2) were split into two because the results were quite different, whereas for broadcaster identification they were aligned across all three measurement questions.

The strength of the pairwise correlations were measured and reported by Qualtrics using p-value, effect size and confidence level of 95%. A correlation is anything with a p value of 0.05 or less. Qualtrics denotes a relationship as subtly positively correlated if it has a p value between 0.05 and approximately 0.01. Anything between 0.01 and 0.00001 is positively correlated and less than 0.00001 is strongly positively correlated. In the tables below, next to each question there is the text of the question, plus quantities of each type of correlated  relationship. A subtly positively correlated result is denoted with SPC, a strong correlation with STRONG and positive correlations are either not noted or noted with a PC.  The total of correlated relationships over total relationships is also denoted in each cell of the matrix (in brackets and italics) to summarize the overall result and these results are described below.

Table 2 shows the results for H1.1, H1.2, H2.1 and H2.2, and Table 3 shows the results for H3.1 and H3.2.

For H1.1, Table 2 shows 3 of 6 measures of information seeking and entertainment motivation were positively correlated to continuous watching intent.

For H1.2, only 1 of a total 8 responses were positively correlated to continuous watching intent.

For H2.1, 11 of a total 12 responses for information seeking and entertainment motivations were positively correlated to Broadcaster Identification. However, the research also reviewed the positive correlations to Social Motivations for Broadcast Identification and 16 of 24 responses were positively correlated.

For H2.2, where the response was feeling like a group of fans of the broadcaster, 7 of 8 responses for measures of social motivations were positively correlated to Group Identification. In addition, responses for 3 of 6 measures of information seeking and entertainment motivation were positively correlated to Group Identification, which is a different relationship to that posited by H2.2.

For H2.2, where the response was feeling like identifying with the broadcasters followers, 7 of 8 responses for measures of social motivations were positively correlated to Group Identification. In contrast to the Fan Club group identification, only 1 of 6 responses for information seeking and entertainment motivation were positively correlated to Group Identification, which does not support a different relationship to that posited by H2.2.

Table 2 Hypotheses and Positively Correlated Questions H1.1, H1.2, H2.1, H2.2

Ta

The results for Hypotheses H3.1 and H3.2 are shown in Table 3, again with H3.2 split for Fans of the Broadcaster and Identifying with other followers.

            For H3.1, the antecedent question responses for Broadcaster identification were correlated for para-social experience in 7 of 9 relationships. In addition, sense of community (13/15), conformity motivation (6/6) and co-experience (8/9) relationships were also positively correlated.

            For H3.2 for the Club of Fans, there was positively correlation across all relationships, para-social experiences (3/3), sense of community (5/5), conformity motivation (2/2) and co-experience (3/3).

            For H3.2 Identifying with other followers group identification, there was positively correlation across almost all relationships, para-social experiences (3/3), sense of community (4/5), conformity motivation (2/2) and co-experience (3/3).

Area#H3.1H.3.2H3.2
  Broadcaster (3)Group- Club of Fans (1)Group -identify with followers (1)
Brief Sense of Community5 fulfill my needs (SPC)+PC I have a say about what goes on x3 People in this Twitch channel are good at influencing each other. I belong in my most watched Twitch channel.(SPC x 2 I have a good bond with others in (SPC+ STRONG_ PC) Fulfil my needs (SPC) Good at influencing each other x1 +SPC (13/15)I have a good bond with others in my most watched Twitch channel. STRONG People in this Twitch channel are good at influencing each other. My most watched Twitch channel helps me fulfill my needs. I belong in my most watched Twitch channel.  I have a say about what goes on in my most watched Twitch channel. (5/5)  I have a say about what goes on in my most watched Twitch channel. I have a good bond with others in my most watched Twitch channel. People in this Twitch channel are good at influencing each other. I belong in my most watched Twitch channel. (4/5)
Cognitive Community1I felt I shared similar thoughts with othe…ence members x 2 (2/3)I felt I shared similar thoughts w (1/1)I felt I shared similar thoughts (1/1)
Conformity motivation2Many people I communicate with watch Twitch.tv x2+SPC Of the people I communicate with regularly; many watch Twitch.tv x2+SPC (6/6)Many people I communicate with watch Twitch.tv Of the people I communicate with regularly; many watch Twitch.tv. (2/2)Many people I communicate with watch Twitch.tvSPC Of the people I communicate with regularly; many watch Twitch.tv. (2/2)
Experience of parasocial interaction3While I was watching, the broadcaster knew that I reacted to them (SPC x 2+ PC) While I was watching, the broadcaster reacted to what I said or did x 3 While I was watching, the broadcaster knew I paid attention to them. (7/9)While I was watching, the broadcaster knew that I reacted to them While I was watching, the broadcaster reacted to what I said or did. While I was watching, the broadcaster knew I paid attention to them. (3/3)While I was watching, the broadcaster reacted to what I said or did.(SPC) While I was watching, the broadcaster knew that I reacted to them. (3/3)
Resonant contagion2My behavior was influenced by others in th…dience group x 2+SPC My behavior influenced others in this audience group x3 (6/6)My behavior influenced others in this audience group of my most watched Twitch channel. My behavior was influenced by othersSTRONG (2/2)My behavior was influenced by others My behavior influenced others (2/2)
Table 2 Hypotheses and Positively Correlated Questions H1.1, H1.2, H2.1, H2.2

Discussion

Support for Hypotheses

The threshold for support is 75% of relationships being correlated.

As support for H1.1 Use of Twitch for information seeking and entertainment motivations are positively correlated with intention of continuation of engagement was 50%, this hypothesis is not supported.

Support for H1.2 1 Use of Twitch for social motivations are positively correlated with intention of continuation of engagement was 16% (1 in 8), this hypothesis is not supported.

Support for H2.1 Use of Twitch for information seeking and entertainment motivations is positively correlated with Broadcaster Identification is 92% (11 in 12), so this hypothesis is supported.

Support for H2.2 Use of Twitch for social motivations is positively correlated with Group Identification where participants felt like a group of fans of the broadcaster was 88% (7 in 8), so this hypothesis is supported. The results for this group using Twitch for information seeking and entertainment motivations were 50% (3 in 6) so do not meet the threshold.

Support for H2.2 Use of Twitch for social motivations is positively correlated with Group Identification where participants identified with other followers of the broadcaster was 88% (7 in 8), so this hypothesis is supported. The results for this group using Twitch for information seeking and entertainment motivations were 16% (1 in 6) so do not meet the threshold.

Support for H3.1 Para-social experience is positively corelated to Broadcaster Identification through Twitch was 78% (7 in 9), so this hypothesis is supported.

Support for H3.2 Conformity Motivation, Sense of Community and Co-experience is positively correlated to Group Identification where participants felt like a group of fans of the broadcaster was 100% (13 in 13), so this hypothesis is supported.

Support for H3.2 Conformity Motivation, Sense of Community and Co-experience is positively correlated to Group Identification where participants identified with other followers of the broadcaster was 92% (12 in 13), so this hypothesis is supported.

Implications

            Unlike other research, a statistically significant relationship could not be found in this survey for watching continuation intention, for neither information seeking, entertainment or social motivations.

 As hypothesized in this research, people who identify with Broadcasters use Twitch for entertainment and information seeking purposes, and those who identify with Groups use Twitch to gratify social motivations.

Unlike Hu et al (Hu et al., 2017), this research found that users who identify with Groups and with the Broadcaster all experience para-social feelings towards the Broadcaster, and also a sense of community with other members, are driven by conformity motivations in their use of the platform, and share a co-experience of Twitch with other users in their group also.

This was a very small sample, so the results are exploratory rather than able to be generalized to a wider population, but it is clear that the respondents of this survey who are users of Twitch have strong social reasons for being on the platform.

Limitations and future directions

The analysis of the contribution of each survey question to the underlying phenomena being measured was very rudimentary i.e based on the proportion of correlated relationships to total relationships. This was due to the limitations of Qualtrics and the researcher’s abilities being limited to that system. Regression analysis was attempted for correlated variables, however the explanatory power was very limited because the sample size was so small. In additional, Qualtrics is unable to conduct contributory factor analysis, so this research relied upon the relationships establishing by the research papers from which the survey questions were drawn (see Table 1). However a lot of these relationships would have been nullified as this is a different sample, and some questions were removed from this survey.

Further analysis could be conducted using a more advanced statistical software package and a larger sample size to overcome these issues.

Conclusions

            This small survey of 87 participants could not establish a relationship between psychological motivations (information seeking, entertainment or social) and watching continuance intention. However, it did support the hypothesis and align with past research that those users who have online social identities aligned to the broadcaster do use the platform for information seeking and entertainment, and those who align their identities with groups of other audience members use Twitch for social motivations.  A novel finding of this research was that whether or not people identified with Group or Broadcasters, they all experience para-social feelings towards the Broadcaster, and also a sense of community with other members, are driven by conformity motivations in their use of the platform, and share a co-experience of Twitch with other users in their group also.

References

Chang, Y. P., & Zhu, D. H. (2011). Understanding social networking sites adoption in China: A comparison of pre-adoption and post-adoption. Computers in Human Behavior, 27(5), 1840–1848. https://doi.org/10.1016/j.chb.2011.04.006

Chiu, C.-M., Hsu, M.-H., & Wang, E. T. G. (2006). Understanding knowledge sharing in virtual communities: An integration of social capital and social cognitive theories. Decision Support Systems, 42(3), 1872–1888. https://doi.org/10.1016/j.dss.2006.04.001

Choe, H. (2019). Eating together multimodally: Collaborative eating in mukbang, a Korean livestream of eating. Language in Society, 48(2), 171–208. https://doi.org/10.1017/S0047404518001355

Ewalt, D. M. (2013). The ESPN Of Video Games. Retrieved December 9, 2019, from Forbes website: https://www.forbes.com/sites/davidewalt/2013/11/13/the-espn-of-video-games/

Ford, C., Gardner, D., Horgan, L., & Liu, C. (2017). Chat Speed OP : Practices of Coherence in Massive Twitch Chat. 12.

Hartmann, T., & Goldhoorn, C. (2011). Horton and Wohl Revisited: Exploring Viewers’ Experience of Parasocial Interaction. Journal of Communication, 61(6), 1104–1121. https://doi.org/10.1111/j.1460-2466.2011.01595.x

Hilvert-Bruce, Z., Neill, J. T., Sjöblom, M., & Hamari, J. (2018). Social motivations of live-streaming viewer engagement on Twitch. Computers in Human Behavior, 84, 58–67. https://doi.org/10.1016/j.chb.2018.02.013

Hu, M., Zhang, M., & Wang, Y. (2017). Why do audiences choose to keep watching on live video streaming platforms? An explanation of dual identification framework. Computers in Human Behavior, 75, 594–606. https://doi.org/10.1016/j.chb.2017.06.006

Jenkins, H. (2006). Fans, bloggers, and gamers exploring participatory culture. New York: New York University Press.

Kang, Y. S., Hong, S., & Lee, H. (2009). Exploring continued online service usage behavior: The roles of self-image congruity and regret. Computers in Human Behavior, 25(1), 111–122. https://doi.org/10.1016/j.chb.2008.07.009

Lim, S., Cha, S. Y., Park, C., Lee, I., & Kim, J. (2012). Getting closer and experiencing together: Antecedents and consequences of psychological distance in social media-enhanced real-time streaming video. Computers in Human Behavior, 28(4), 1365–1378. https://doi.org/10.1016/j.chb.2012.02.022

Liu, S., Liao, J., & Wei, H. (2015). Authentic Leadership and Whistleblowing: Mediating Roles of Psychological Safety and Personal Identification. Journal of Business Ethics, 131(1), 107–119. https://doi.org/10.1007/s10551-014-2271-z

Masayuki Yoshida, Bob Heere, & Brian Gordon. (2015). Predicting Behavioral Loyalty through Community: Why Other Fans Are More Important Than Our Own Intentions, Our Satisfaction, and the Team Itself. Journal of Sport Management, 29(3), 318–333. https://doi.org/10.1123/jsm.2013-0306

Mcmillan, D., & Chavis. (1986). Sense of community: A definition and theory. 18.

Pendry, L. F., & Salvatore, J. (2015). Individual and social benefits of online discussion forums. Computers in Human Behavior, 50, 211–220. https://doi.org/10.1016/j.chb.2015.03.067

Peterson, N., Speer, P., & Mcmillan, D. (2008). Validation of a Brief Sense of Communtiy Scale: Confirmation of the Principal Theory of Sense of Community. Journal of Community Psychology, 36, 61–73. https://doi.org/10.1002/jcop.20217

Postmes, T., Spears, R., & Lea, M. (1998). Breaching or Building Social Boundaries?: SIDE-Effects of Computer-Mediated Communication. Communication Research, 25(6), 689–715. https://doi.org/10.1177/009365098025006006

Shamir, B., Zakay, E., Breinin, E., & Popper, M. (1998). Correlates of Charismatic Leader Behavior in Military Units: Subordinates’ Attitudes, Unit Characteristics, and Superiors’ Appraisals of Leader Performance. Academy of Management Journal, 41(4), 387. https://doi.org/10.2307/257080

Tajfel, & Turner. (1979). An integrative theory of intergroup conflict.

Twitch. (2019). Twitch Press Center. Retrieved December 9, 2019, from Press Center website: https://www.twitch.tv/p/press-center/

WALTHER, J. B. (1996). Computer-Mediated Communication: Impersonal, Interpersonal, and Hyperpersonal Interaction. Communication Research, 23(1), 3–43. https://doi.org/10.1177/009365096023001001

Wikipedia. (2019). Twitch (service). In Wikipedia. Retrieved from https://en.wikipedia.org/w/index.php?title=Twitch_(service)&oldid=930037208

Xiao, H., Li, W., Cao, X., & Tang, Z. (2012). The Online Social Networks on Knowledge Exchange: Online Social Identity, Social Tie and Culture Orientation. Journal of Global Information Technology Management, 15(2), 4–24. https://doi.org/10.1080/1097198X.2012.11082753

Complete list of Questions

AreaQuestionReferenceRemoved?
Broadcaster identification (BRI)I am proud to be the broadcaster’s follower.  (Hu et al., 2017; Liu, Liao, & Wei, 2015; Shamir, Zakay, Breinin, & Popper, 1998)No
Broadcaster identification (BRI)The broadcaster represents values that are important to me.  (Hu et al., 2017; Liu et al., 2015; Shamir et al., 1998)No
Broadcaster identification (BRI)My values are like the broadcaster’s values.  (Hu et al., 2017; Liu et al., 2015; Shamir et al., 1998)Removed
Broadcaster identification (BRI)The broadcaster is a model for me to follow.  (Hu et al., 2017; Liu et al., 2015; Shamir et al., 1998)No
Brief Sense of CommunityI can get what I need in this Twitch channel.  (Hilvert-Bruce et al., 2018; Mcmillan & Chavis, 1986; Peterson et al., 2008)Removed
Brief Sense of CommunityThis Twitch channel helps me fulfill my needs.(Hilvert-Bruce et al., 2018; Mcmillan & Chavis, 1986; Peterson et al., 2008)No
Brief Sense of CommunityI feel like a member of this Twitch channel.  (Hilvert-Bruce et al., 2018; Mcmillan & Chavis, 1986; Peterson et al., 2008)Removed
Brief Sense of CommunityI belong in this Twitch channel.(Hilvert-Bruce et al., 2018; Mcmillan & Chavis, 1986; Peterson et al., 2008)Removed
Brief Sense of CommunityI have a say about what goes on in my Twitch channel.  (Hilvert-Bruce et al., 2018; Mcmillan & Chavis, 1986; Peterson et al., 2008)No
Brief Sense of CommunityPeople in this Twitch channel are good at influencing each other.    (Hilvert-Bruce et al., 2018; Mcmillan & Chavis, 1986; Peterson et al., 2008)No
Brief Sense of CommunityI feel connected to this Twitch channel.  (Hilvert-Bruce et al., 2018; Mcmillan & Chavis, 1986; Peterson et al., 2008)Removed
Brief Sense of CommunityI have a good bond with others in this Twitch channel.  (Hilvert-Bruce et al., 2018; Mcmillan & Chavis, 1986; Peterson et al., 2008)no
Conformity motivationMany people I communicate with watch Twitch.  (Chang & Zhu, 2011)No
Conformity motivationThe people I communicate with will continue to watch Twitch.(Chang & Zhu, 2011)Removed
Conformity motivationOf the people I communicate with regularly; many watch Twitch.  (Chang & Zhu, 2011)No
Cognitive CommunionI felt I shared similar thoughts with other audience members.(Hu et al., 2017; Lim et al., 2012)No    
Cognitive CommunionI felt my knowledge was shared with other audience members.(Hu et al., 2017; Lim et al., 2012)Removed
Cognitive CommunionI felt I shared the same perspective as other audience members.(Hu et al., 2017; Lim et al., 2012)Removed
Continuous watching intention (CWI)I intend to continue to watch Twitch.tv within the next two months.  (Chang & Zhu, 2011; Hu et al., 2017; Kang, Hong, & Lee, 2009)No
Continuous watching intention (CWI)I intend to continue following the broadcaster rather than discontinuance.(Hu et al., 2017; Kang et al., 2009)Removed
Continuous watching intention (CWI)I intend to continue watching the broadcaster’s channel rather than other alternatives.  (Hu et al., 2017; Kang et al., 2009)No
Entertainment MotivationI watch Twitch.tv to pass the time.(Chang & Zhu, 2011; Hilvert-Bruce et al., 2018)Removed
Entertainment MotivationI watch Twitch.tv to entertain myself with games.(Chang & Zhu, 2011; Hilvert-Bruce et al., 2018)Removed
Entertainment MotivationI watch Twitch.tv for fun.(Chang & Zhu, 2011; Hilvert-Bruce et al., 2018)No
Experience of parasocial interactionWhile I was watching, the broadcaster knew I paid attention to them.(Hartmann & Goldhoorn, 2011; Hu et al., 2017)No
Experience of parasocial interactionWhile I was watching, the broadcaster knew that I reacted to them.(Hartmann & Goldhoorn, 2011; Hu et al., 2017)No
Experience of parasocial interactionWhile I was watching, the broadcaster reacted to what I said or did.(Hartmann & Goldhoorn, 2011; Hu et al., 2017)No
Channel size preferencePlease estimate the number of viewers who are typically in the Twitch channel(s) you spend the most time viewing.   Small 500 or less Large 501 to 10000 Very Large over 10000  (Hilvert-Bruce et al., 2018)No
 My friends (in real life) and I like to watch the same channels.MeRemoved
 My friends (in real life) and I like to chat in stream chats on Twitch.tv.  MeRemoved
Meeting new peopleI meet new people in stream chat rooms.(Chang & Zhu, 2011)No
   I like to follow certain broadcasters’ channels.  MeNo
 I like to follow channels related to a certain game.MeNo
Group identification (GRI)I really identify with people who follow the broadcaster.(Hu et al., 2017; Masayuki Yoshida, Bob Heere, & Brian Gordon, 2015)No
Group identification (GRI)I feel like I belong to a club with other fans of the broadcaster.(Hu et al., 2017; Masayuki Yoshida et al., 2015)No
Group identification (GRI)The broadcaster is supported by people like me.(Hu et al., 2017; Masayuki Yoshida et al., 2015)Removed
Information motivation (IΜ)  I watch Twitch.tv to learn about things I don’t know.(Chang & Zhu, 2011; Hilvert-Bruce et al., 2018)No
Information motivation (IΜ)  I watch Twitch.tv to search for information I need.(Chang & Zhu, 2011; Hilvert-Bruce et al., 2018)Removed
Information motivation (IΜ)  I watch Twitch.tv to keep up to date.  (Chang & Zhu, 2011; Hilvert-Bruce et al., 2018)Removed
Information motivation (IΜ)  I watch Twitch.tv get useful information.  (Chang & Zhu, 2011; Hilvert-Bruce et al., 2018)No
Online Social IdentityI know the screen names of others in this stream chat room.(Postmes et al., 1998; WALTHER, 1996; Xiao et al., 2012)No
Online Social IdentityI know the real names of others in this stream chat room.(Postmes et al., 1998; WALTHER, 1996; Xiao et al., 2012)No
Online Social IdentityI know the personalities of others in this stream chat room.(Postmes et al., 1998; WALTHER, 1996; Xiao et al., 2012)No
Online Social IdentityOthers in this stream chat room know my screen name.(Postmes et al., 1998; WALTHER, 1996; Xiao et al., 2012)No
Online Social IdentityOthers in this stream chat room know my real name.(Postmes et al., 1998; WALTHER, 1996; Xiao et al., 2012)No
Online Social IdentityOthers in this stream chat room know my personality.(Postmes et al., 1998; WALTHER, 1996; Xiao et al., 2012)No
Online Social TieI frequently interact with others in the Twitch community.(Xiao et al., 2012)  No Modified from a free text question
Engagement indicatorI frequently watch Twitch.tv.    (Hilvert-Bruce et al., 2018)No Modified from a free text question
Resonant contagion (REC)My behavior was influenced by others in this audience group.    (Hu et al., 2017; Lim et al., 2012)No
Resonant contagion (REC)My behavior influenced others in this audience group.(Hu et al., 2017; Lim et al., 2012)No
Resonant contagion (REC)Our audience group agreed upon similar opinions.(Hu et al., 2017; Lim et al., 2012)Removed
 Our audience group engages in ASCII art chat.    MERemoved  
 Our audience group engages in copypasta chat.  MENo
 What is your age?   Under 18 18 – 24 25 – 34 35 – 44 45 – 54 55 – 64 65 – 74 Over 74      MENo
 55. What is your gender?   Female Male Other Prefer not to say  MENo
Complete List of Questions

Leave a comment

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

%d bloggers like this: