Full length articleWhy do audiences choose to keep watching on live video streaming platforms? An explanation of dual identification framework
Introduction
In the decade, Internet users have become keen on communication through various social media services such as virtual communities, SNS websites, and blogs (Kaplan & Haenlein, 2010). Recently, the forms of computer-mediated communications have been extended beyond text and image to audio and video as results of the revolution of cutting edge internet technologies. In specific, a unique form of social media has emerged, and been recognized as live video streaming platforms. As a special combination of multiple media forms, live streaming allows individuals to publicly broadcast live video streams, accompany with a shared chat room for user communication (Hamilton, Garretson, & Kerne, 2014). Generally, a typical live video streaming activity involves a streamer/broadcaster who uploads his/her real-time video and audio content including video games, talent performance, daily life, or whatever he/she expects to share. Viewers/audiences on the streamer's channel can comment and communicate with each other via text-based chat room function. Meanwhile, the streamer also engages in dialogues and interactions with his/her audiences while broadcasting.
Live streaming activity has witnessed its prosperity since the availability of diverse platforms such as Twitch (a famous video game streaming website) and YouTube Live (live streaming service of YouTube) (Smith, Obrist, & Wright, 2013). For instance, Twitch made up 1.8% of total US Internet traffic and ranked at the fourth during peak periods in 2014 (Pires & Simon, 2015). Twitch also announced it had more than 1.5 million broadcasters and over 100 million visitors per month in 2015 (Needleman, 2015). It seems that more and more people are becoming immersed in this live video watching. Hence, we can't help wondering what the rationale is behind audiences' real-time video watching behavior on live streaming platforms.
Compared with flourishing development in practice field, academic realm has paid unequal attention to live video streaming activity. Users' continuance intention on other type of social media, such as virtual communities, social networks, has received sufficient attention in academic domain (e.g. Lin et al., 2014, Zheng et al., 2013). However, most studies in computer science take a technological approach to optimize streaming network systems, or try to demonstrate the characteristics of streams on some famous platforms. (e.g. Barekatain et al., 2015, Kaytoue et al., 2012, Pires and Simon, 2015). Extant literature still lacks a comprehensive framework to explain audiences' continuous watching behavior. Only limited studies have shed light on this question. On one hand, the presence of co-viewers and sense of community within streaming channels have been regarded as necessary elements; on the other hand, frequent interactions between broadcasters and audiences have been emphasized in attracting and maintaining audiences as well (Hamilton et al., 2014, Lim et al., 2012, Smith et al., 2013). Thereby, audiences’ continuous watching intention may be explained in a social-psychological approach which considers both audience-broadcaster tie and audience-other audiences tie.
In fact, the nature of live video streaming activity not only offers a real-time watching experience for audiences, but also provides opportunities to communicate and socialize among broadcasters and other co-viewers. These interactions in cyber contexts may promote the development of audiences’ self-definition process and further identifications with various referents (Hall-Phillips, Park, Chung, Anaza, & Rathod, 2016). Also, the psychological bond of a social referent is an important predictor of loyalty behavior within virtual communities (Badrinarayanan, Sierra, & Martin, 2015). Thus, incorporating the identification concept in current study may be helpful to explain the study question.
Drawing on previous research, this study aims to develop and empirically test an audiences' social identification framework based on social identity theory. Firstly, a dual identification model, which depicts identifications with broadcasters and audience groups, is proposed to explain users' continuous watching intention. Secondly, unlike most prior studies which take identification for granted without considering its formation in computer-mediated communications (Shen & Khalifa, 2015, pp. 87–101; Tuškej, Golob, & Podnar, 2013), we expect to carefully examine the antecedents of identification process. Identification can be either deduced from collective identity or inter-member interaction (Postmes, Spears, Lee, & Novak, 2005). Previous studies mainly take the former perspective to explore the antecedents of identity attractiveness that leads to identification (e.g. Bhattacharya & Sen, 2003). However, in consideration of the interactive nature on live streaming platforms, perceived identity information is mainly rooted in interaction process among broadcasters and audiences. Audiences' identifications with various referents may result from interactive experience between members. Hence, we intend to inspect the formation of audiences' identifications from the perspective of user experiences that generate from interactions within audience-broadcaster tie and audience-other audiences tie. Current study constructs two categories of experiences to describe audiences’ sensations and feelings regarding to live video consumption. Specifically, parasocial interaction experience and self-congruity have been conceptualized as the individual experience resulting from interaction with broadcasters. Participation, cognitive communion, and resonant contagion constitute the co-experience which is introduced to measure experiences emerging during interaction with other audiences. Finally, to further understand the potential influence of streaming content genres in proposed dual identification framework, a sub-group comparison involving two popular stream categories (video games & talent shows) is conducted.
Section snippets
Theoretical background: social identity theory
Proposed by Tajfel and Turner (1979), social identity theory posits that people hold various social identities along with their individual identities. It is assumed that our self-concepts are partially defined by certain social groups where we obtain the sense of oneness or belongingness, as well as involving values (Ashforth & Mael, 1989). Hence, people tend to classify themselves into various social categories that they identify with, and develop social identifications which depict the
Research methods
In order to examine our proposed research framework and hypotheses empirically, we conducted an online survey among live video streaming platform audiences in mainland China. A multi-item scales questionnaire based on prior literature was constructed. After the validation of reliability and validity of the scales, data analysis was carried out with the tool of SmartPLS 2.0.
Data analysis and results
We adopted the partial least squares (PLS) approach to evaluate the measurement and structural model. Specifically, we conducted data analysis with the software of SmartPLS 2.0 in this study.
Theoretical implication
The current study contributes to extent literature on live video streaming research in following aspects. First, live streaming activity, as a newly emerged phenomenon, hasn't received adequate attention in academic domain. To the best of our knowledge, current study is among the first to investigate watching behavior on live streaming platforms from a social-psychological and empirical perspective. Specifically, based on social identity theory, we constructed a dual identification framework to
Limitation and future research
Several limitations exist in this study. First, it is acknowledged that the research context selection and data collection process might restrict the generalizability of the results. We conducted the investigation in mainland China and collect data from two live streaming platforms. Future studies are suggested to extend current research scope to including other platforms. Meanwhile, cross-national analysis is encouraged to offer a more inclusive understanding of live streaming phenomenon.
Acknowledgement
The authors gratefully acknowledge the support of National Natural Science Foundation of China (No. 71272018), and the useful suggestions by the editor and reviewers.
Mu Hu is a PhD Candidate at the School of Economics and Management in Beihang University, Beijing, China. His research interest comprises social commerce and entrepreneurial management. His work has been published in Computers in Human Behavior, International Journal of Information Management, and other journals.
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Mu Hu is a PhD Candidate at the School of Economics and Management in Beihang University, Beijing, China. His research interest comprises social commerce and entrepreneurial management. His work has been published in Computers in Human Behavior, International Journal of Information Management, and other journals.
Mingli Zhang is a professor at the School of Economics and Management in Beihang University, Beijing, China. His main research interest has been in the area of customer behavior in e-marketing, branding strategies, and customer value. His work has been published in Internet Research, Computers in Human Behavior, International Journal of Information Management, Journal of Business-to-Business Marketing, Psychology & Marketing, and other journals.
Yu Wang is a PhD candidate at the School of Economics and Management, Beihang University, Beijing, China. Her research centers on information technology, social science and digital marketing. Her work has been published in Computers in International Journal of Information Management.