Crowding-Out in Content Monetization Under Pay What You Want: Evidence From Live Streaming

Abstract

Live streaming has emerged as an innovative means for content providers (broadcasters) to monetize their content in real time under pay-what-you-want pricing. In a typical live stream, consumers (viewers) watch the content and decide whether and how much to tip the broadcaster in the form of virtual gifts that have been purchased with real money. Unlike offline contexts where payment is often nontransparent, both payment activities and sender identities are transparent or publicly observable in live streams. Hence, understanding to what extent and how tipping influences broadcasters’ emotional reactions and peer viewers’ engagement activities becomes relevant and meaningful. In this study, we examine the social impact of viewer tipping activity by running a field experiment on a popular live-streaming platform in China. We deploy synthetic viewers to both treated and control streams. These synthetic viewers send random tip amounts at random times in only the treated and not the control streams, which then exogenously alters the tips that are observed by the audience. We find that broadcasters tend to provide an emotional and reciprocal reaction in response to additional viewer tips, which is measured by the broadcasters’ level of happiness as discerned from their facial expressions. Viewers tend to tip less, chat less, and leave the current stream sooner when seeing more tips from peers, suggesting a negative crowding-out effect on viewer engagement. Nevertheless, the crowding-out effect does not apply to the number of likes, which are displayed without viewer identities in a live stream. In addition, such crowding-out effects manifest mainly in those viewers who tipped heavily before the experiment, possibly because heavy tippers care more about social status than their counterparts. These results collectively suggest that the pursuit of social status is a plausible driver of the observed crowding-out effects.