Quantifying the Impact of Viewer Interactions on Consumer Engagement Metrics in Live Streaming E-Commerce on Taobao Live

This study examines how specific real-time viewer interactions, namely likes and comments, shape consumer engagement metrics within the context of live-streaming e-commerce on Taobao Live. Grounded in Gratifications Theory and Media Richness Theory, the research investigates whether these two interaction types exert differential effects on viewer count and watch duration, two key indicators of engagement. To address these questions, a quantitative dataset comprising 150 live-stream sessions from Taobao Tmall was collected and analyzed. Descriptive statistics outlined the variability in interaction and engagement behaviors, while Pearson correlation coefficients assessed bivariate relationships among variables. Subsequently, multiple regression models were employed to predict viewer count and watch duration based on likes and comments, while controlling for session length, average product price, and total product count. Results reveal that both likes and comments exhibit statistically significant positive associations with consumer engagement metrics; however, comments consistently emerge as the stronger predictor, exerting a more substantial influence on both the size of the audience and its duration of attention. These findings underscore the importance of high-effort, dialogic interactions in fostering deeper user engagement, supporting theoretical assertions regarding the role of social presence and cognitive elaboration in live-streaming contexts. Results indicate that encouraging active, comment-based interaction offers a more effective strategy than relying solely on passive indicators such as likes, providing actionable insights for broadcasters and platform designers seeking to optimize viewer participation and retention in live-stream e-commerce environments.