@article{65514c9e7b1a45f889a2eb23d9fbb00c,
title = "Happiness Begets Money: Emotion and Engagement in Live Streaming",
abstract = "Live streaming offers an unprecedented opportunity for content creators (broadcasters) to deliver their content to consumers (viewers) in real time. In a live stream, viewers may send virtual gifts (tips) to the broadcaster and engage with likes and chats free of charge. These activities reflect viewers{\textquoteright} underlying emotion and are likely to be affected by the broadcaster{\textquoteright}s emotion. This article examines the role of emotion in interactive and dynamic business settings such as live streaming. To account for the possibility that broadcaster emotion, viewer emotion, and viewer activities influence each other, the authors estimate a panel vector autoregression model on data at the minute level from 1,450 live streams. The results suggest that a happier broadcaster makes the audience happier and begets intensified viewer activities, in particular tips. In addition, broadcasters reciprocate viewer engagement with more smiles. Further analyses suggest that these effects are pronounced only after a live stream has been active for a while, and they manifest only in streams by broadcasters who have more experience, receive more tips, or are more popular in past live streams. These results help platforms and broadcasters optimize marketing interventions such as broadcaster emotion enhancement in live streaming and quantify the financial returns.",
keywords = "digital marketing, emotion, engagement, live streaming, social media, text analysis, video analysis",
author = "Yan Lin and Dai Yao and Xingyu Chen",
note = "Funding Information: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research is supported by the Start-up Research Grant of Shenzhen University (00001109), the NUS Research Grant (R-316-000-104-133), the Natural Science Foundation of Guangdong Province, China (2019WQNCX106), and the National Natural Science Foundation of China (71872115). Funding Information: The authors thank a large live streaming platform in China for help with the collection of the data and extensive discussions about the industry. They are also thankful to the JMR review team for the constructive and developmental review process. The authors also thank the participants at the 2019 Conference on Artificial Intelligence, Machine Learning, and Business Analytics at Temple University; the 18th Pre-ICIS Workshop on e-Business; the 2020 Marketing Science Conference; Peking University; Tsinghua University; Shanghai Jiaotong University; City University of Hong Kong; and Hong Kong Polytechnic University for their valuable feedback. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research is supported by the Start-up Research Grant of Shenzhen University (00001109), the NUS Research Grant (R-316-000-104-133), the Natural Science Foundation of Guangdong Province, China (2019WQNCX106), and the National Natural Science Foundation of China (71872115). Publisher Copyright: {\textcopyright} American Marketing Association 2021.",
year = "2021",
month = jun,
doi = "10.1177/00222437211002477",
language = "English",
volume = "58",
pages = "417--438",
journal = "Journal of Marketing Research",
issn = "0022-2437",
publisher = "American Marketing Association",
number = "3",
}