题　目：Do Expressive Behaviors Matter? The Effects of Broadcasters’ Visual Expressiveness and Emotion Transitions on Viewership Engagement in Social Mobile Live Streaming

嘉　宾：Yutong Guo, Ph.D. Candidate, National University of Singapore

主持人：宋婷婷  副教授  上海交通大学安泰经济与管理学院

时　间：2022年11月15日（周二） 10:00-11:30

地　点：腾讯会议 (校内师生如需会议号和密码，请发送电邮至xuziqing@sjtu.edu.cn获取)

内容简介:

This paper aims to investigate the underexplored but critical role of broadcasters’ expressive behaviors in influencing viewership engagement in terms of gift-sending behaviors in the context of social mobile live streaming. Drawing on the social psychological theories of interpersonal communication, we specifically focus on two aspects of broadcasters’ expressive behaviors – visual expressiveness and emotion transitions. By utilizing live streaming videos and viewers’ gift-sending data from a mobile-based social live streaming application, we algorithmically extract the features of human visual displays and facial emotions with computer vision techniques and empirically quantify their effects on virtual gifts given using multi-level mixed-effects model estimations. Our empirical analyses find that although broadcasters’ hands display does not relate to gift-sending behaviors, there exists a positive association between broadcasters’ face display and gifts given. Moreover, we find that a one-percent increase in the weighted frequency of broadcasters’ positive (negative) emotion transitions is associated with a 0.29% (0.14%) increase (decrease) in the monetary value of gifts. Notably, these relationships primarily stem from emotion transitions to long-lived positive emotions and short-lived negative emotions. Our study provides novel empirical findings and associated theoretical explanations, as well as important practical implications for live streaming platforms and broadcasters.

演讲人简介：

Yutong Guo is a Ph.D. candidate in the Department of Information Systems and Analytics at the School of Computing at the National University of Singapore. Her research focuses on understanding various consumer and firm behaviors in social-mobile technologies and digital marketing communications. Methodologically, she implements machine learning-based algorithms to extract variables from consumer- and marketer-generated unstructured data (e.g., text, image, audio, video). She primarily employs micro-econometrics to analyze actual behavioral data. Her research has been published/presented at several international conferences (e.g., ICIS, CIST, PACIS).

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