To Gift or Not: Understanding Gifting Behavior on Live Streaming Platforms from the Perspective of Social Influence and Herding

Abstract

Live streaming has been increasingly popular worldwide, with gift-giving emerging as a pivotal revenue stream for many streamers. While prior research has delved into the influence of streamers’ emotions and viewer-streamer interaction on viewers’ gift-giving behaviors, we suggest that peer viewers also play an essential role. In line with the principles of social influence and herding theory, the behaviors of peer viewers and the size of the viewing group are integral factors shaping individual behaviors. Hence, in the context of lives streaming, we focus on examining the impact of peer viewers’ gift-giving behaviors and the audience size on the gift-giving behaviors of individual viewers, respectively. Additionally, we examine the moderating role of viewers’ identities. We collected data from a popular live streaming platform in China and employed a panel regression model based on a sample of 651,678 viewers. This study contributes to the gift-giving literature by revealing the influence of peer viewers on focal viewers’ likelihood of gifting, gifting frequency and gifting value, and the moderating effect of viewers’ identities. Overall, these results have significant implications for both the theoretical understanding of social influence and herding in online setting, as well as the practical implications for future live streaming management. Future research could delve deeper into understanding the impact of various types of live streaming content and cultural differences on individual’s gift-giving behavior.

Keywords:

• Live streaming
• social influence
• herding
• identity-based motivation

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 https://www.twitch.tv/p/press-center/.

2 https://www.cnnic.com.cn/IDR/ReportDownloads/202311/P020231121355042476714.pdf#page=42&zoom=100,116,346.

3 a type of real-time comment sent by viewers and scrolled across the screen.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (Nos. 72101157, 71942003), Shanghai International Studies University Youth Research Grant (No. 41004816), MOE (Ministry of Education in China) Project of Humanities and Social Sciences (No. 23YJC630260), Shanghai Pujiang Programme (No. 23PJC089) and the Fundamental Research Funds for the Central Universities (No. 2023TD003).

Notes on contributors

Lijuan Luo

Lijuan Luo is currently an associate professor at Shanghai International Studies University, China. She received her Ph.D. in management science and engineering from Beijing University of Posts and Telecommunications, China, in 2015. Her research interests include information management, machine learning, AI and human-computer interaction.

Shen Hanyi

Shen Hanyi received her BA degree from Shanghai International Studies University, China. She is now working toward the master degree at Shanghai International Studies University, China. Her research interests include online review, AI chatbot, user-generated content and consumer behavior.

Yujie Zheng

Yujie Zheng is currently an assistant professor at Shanghai International Studies University, China. She received her Ph.D. in Information systems from Nanyang Technological University, Singapore. Her research interests include block chain, AI chatbot, human-computer interaction and consumer behavior.

Yufei Yuan

Yufei Yuan is a professor of information systems at the DeGroote School of Business at Mcmaster University. His expertise is in mobile commerce, information security and privacy, business analytics, online negotiation, human-computer interaction, and decision-making in health care. He is also investigating the application of online cognitive behavioural therapy (CBT).