What drives users to tip? The impact of contributor experience, content length, and content type on online video sharing platforms

Abstract

This study examines what drives user tipping on online video sharing platforms, focusing on how video characteristics such as contributor experience, content length, and content type affect tipping behavior. Drawing from theoretical foundations such as the elaboration likelihood model and social exchange theory, we propose several hypotheses. Based on a dataset from Bilibili.com, we employ econometric models to test the proposed hypothesis. We find that contributor experience generally decreases tipping, while longer videos tend to attract more tips. Additionally, users are more likely to tip for entertainment content over knowledge content, demonstrating a preference for hedonic values. Furthermore, the effects of contributor experience and content length on tipping are less pronounced for knowledge content than for entertainment content, showing different impacts based on content type. Our findings provide insight into the complex dynamics of viewer engagement and tipping on digital platforms.