网红直播打赏收入影响因素的实证研究

网红经济是一种新型经济模式,打赏也成为消费者支配财富的一种新型方式。探索打赏背后的影响因素和经济规律不仅对网红娱乐平台的可持续发展至关重要,且可为相关监管规定的落实和完善提供有益参考,有助于引导“网红经济”持续健康发展。当前学术界尚缺乏探索网红主播打赏收入影响因素的研究,本文获取了5家网红经纪机构中41位游戏类网红主播每场直播的收入与直播时长数据,研究发现:首先,网红主播的直播收入和观众打赏强度均与直播时长显著正相关,这意味着网红主播对观众的更多娱乐陪伴使其获得了更多的打赏收入;其次,头部打赏者对网红主播的打赏使非头部打赏者的打赏强度更大,这表明观众对网红主播的打赏存在“羊群效应”。

Factors Affecting Online Celebrities' Tips

Rapid economic growth has led to an increase in residents' wealth, and how people dispose of their wealth has become an increasingly important research topic. In recent years, the online celebrity economy has developed rapidly, and tipping live stream performers, a new wealth disposal method, has attracted attention. What motivates audiences to tip online celebrities？ What are the rules of audience tipping behavior？ This research is quite important, because a better understanding of tipping behavior is necessary to guide the healthy development of this industry. On the one hand, tipping plays an important role in the online celebrity economy, so exploring the influencing factors behind tipping is conducive to the sustainable development of the online celebrity economy. On the other hand, the online celebrity economy also appears some chaos, so the study of tipping behavior can provide theoretical support for the formulation of regulatory rules. However, a lack of data has limited research on this topic. This study uses a unique dataset from five multiple-channel network (MCN) agencies to examine this issue. The dataset from these five MCN agencies consists of panel data from 2019 on the income and duration of each live stream of 41 online celebrities who play the same game. Two main findings are made. First, the entertainment accompany of online celebrities increases both the speed of accrual and the amount of the celebrities' income. In other words, longer live streams generate higher incomes. The intensity of tipping also increases with the length of the live stream. Thus, the entertainment accompany of online celebrities satisfies the spiritual needs of the audience, making the audience more likely to tip. In the analysis, the influence of the “star-making” activities of MCN agencies is eliminated by deleting the most popular online celebrities to test the robustness of the conclusion. Second, there is a significant positive correlation between head-tippers and non-head-tippers. That is, there is a herding effect in audiences' tipping behavior. To eliminate the concern of a false regression, the live experience of online celebrities is used as a heterogeneity test.The findings partially explain the rapid development of the online celebrity industry; the interactions between celebrities and audiences and between audience members jointly promote the rapid growth of the online celebrity economy. As companionship encourages audience members to tip, celebrities can increase their income by increasing the duration of their live streams. Furthermore, the tipping of head tippers influences that of non-head tippers and vice versa, allowing the celebrities' income to continue to grow. Therefore, the online celebrity economy demonstrates characteristics of the “Matthew Effect.”This study makes several contributions to the literature. First, it adopts a micro-research perspective on the factors that influence online tipping, which will contribute to future research on the online celebrity economy. Second, this study is one of the first to use the income data of online celebrities. The unique and rigorous dataset obtained from MCN agencies is not only the basis for credible conclusions, but it also provides a basic framework for future research on the online celebrity economy. Third, it separately examines the tipping behavior of head tippers and non-head tippers, verifying the existence of the herding effect. Finally, this study provides new empirical evidence for the Matthew Effect.Due to the limited number and dimensions of cooperation samples, a detailed exploration of the online celebrity economy is not provided in this study, leaving some problems for future research. For example, the dataset does not include information on each audience member's tipping behavior, making it impossible to analyze audience members' motivations in detail. Furthermore, there are many types of online celebrities and this study does not consider the factors that affect the income of online celebrities in other fields. Finally, different platforms may have different share ratios and platform rules, creating strong heterogeneity in the relationships between viewers and streamers on different platforms. Although it does not address the above issues, this study begins the development of principles for understanding the new economic model represented by the online celebrity economy and provides a reference for institutions seeking to guide and regulate such economic activities, ensuring their healthy development.