Summary:
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.
[1] 郝晓玲和陈晓梦,2019,《体验型产品消费行为的羊群效应及机理研究——基于电影行业消费行为的实证解释》,《中国管理科学》 第11期,第176页~188页。 [2] 廖理、李梦然、王正位和贺裴菲,2015,《观察中学习:P2P网络投资中信息传递与羊群行为》,《清华大学学报(哲学社会科学版)》第1期,第156页~165页。 [3] 廖理、向佳和王正位,2018,《P2P借贷投资者的群体智慧》,《中国管理科学》第10期,第30~40页。 [4] Andreoni, J.,1989,“Giving with Impure Altruism: Applications to Charity and Ricardian Equivalence”, Journal of Political Economy, 97(6), pp.1447~1458. [5] Andreoni, J., 1990, “Impure Altruism and Donations to Public Goods: A Theory of Warm-glow Giving”, The Economic Journal, 100(401), pp.464~477. [6] Autor, D., D., Dorn, L. F., Katz, C., Patterson, and J. V., Reenen, 2017, “Concentrating on the Fall of the Labor Share,” American Economic Review Papers and Proceedings, 107, pp.180~185. [7] Autor, D., D., Dorn, L. F., Katz, C., Patterson, and J. V., Reenen, 2020, The Fall of the Labor Share and the Rise of Superstar Firms, The Quarterly Journal of Economics, 135(2), pp.645~709. [8] Bagwell, L. S., and B. D., Bernheim, 1996, “Veblen Effects in a Theory of Conspicuous Consumption”, The American Economic Review, pp.349~373. [9] Banerjee, A. V., 1992, “A Simple Model of Herd Behavior”, The Quarterly Journal of Economics, 107(3), pp.797~817. [10] Bessen, J., 2020, “Industry Concentration and Information Technology” ,The Journal of Law and Economics, 63(3), pp.531~555. [11] Bikhchandani, S., D. Hirshleifer, and I. Welch, 1992, “A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades”, Journal of Political Economy, 100(5), pp. 992~1026. [12] Corneo, G., and O., Jeanne, 1997, “Conspicuous Consumption, Snobbism and Conformism” , Journal of Public Economics, 66(1), pp.55~71. [13] Grinblatt, M., S. Titman and R. Wermers, 1995, “Momentum Investment Strategies, Portfolio Performance, and Herding: A Study of Mutual Fund Behavior”, The American Economic Review, 85(5), pp.1088~1105. [14] Katz, M.; and C., Shapiro, 1985, “Network Externalities, Competition, and Compatibility”, The American Economic Review, 75 (3), pp.424~440 [15] Katz, M.; and C., Shapiro, 1994, “Systems Competition and Network Effects”, Journal of Economic Perspectives, 8 (2), pp.93~115. [16] Ribar, D. C., and M. O., Wilhelm, 2002, “Altruistic and Joy-of-giving Motivations in Charitable Behavior”, Journal of Political Economy, 110(2), pp.425~457. [17] Shiller, R. J., 1995, “Conversation, Information, and Herd Behavior”, The American Economic Review,85(2), pp.181~185.