Pre-conference talk for SONG Danyang and ZHANG Hao

Talk #1: What Should Streamers Communicate in Livestream E-Commerce? The Effects of Social Interactions on Livestreaming Performance
by SONG Danyang, PhD Candidate
for International Conference on Information Systems 2022 (ICIS 2022)

Compared with traditional e-commerce, livestreaming e-commerce is characterized by direct and intimate communication between streamers and consumers that stimulates instant social interactions. This study focuses on streamers’ three types of information exchange (i.e., product information, social conversation, and social solicitation) and examines their roles in driving both short-term and long-term livestreaming performance (i.e., sales and customer base growth). We find that the informational role of product information (nonpromotional and promotional) is beneficial not only to sales performance, but also to the growth of the customer base. We also find that social conversation has a relationship-building effect that positively impacts both sales and customer base growth, whereas social solicitation has both a relationship-building and a relationship-straining effect that positively affects customer base growth but can hurt sales. Furthermore, our results show that streamers’ social interactions with consumers can stimulate consumer engagement in different ways, leading to different effects on livestreaming performance.

Talk #2: Personalized Recommendation for Balancing Content Generation and Usage on Two-Sided Entertainment Platforms
by ZHANG Hao, PhD Student
for International Conference on Information Systems 2022 (ICIS 2022)

Online entertainment platforms such as Youtube host a vast amount of user-generated content (UGC). The unique feature of two-sided UGC entertainment platforms is that creators’ content generation and users’ content usage can influence each other. However, traditional recommender systems often emphasize content usage but ignore content generation, leading to a misalignment between these two goals. To address the challenge, we propose a prescriptive uplift framework to balance content generation and usage through personalized recommendations. Using a large-scale real-world dataset, we demonstrate that the proposed recommendation method better balances content generation and usage and brings a significant increase in participants’ activity compared to existing benchmark methods.

Danyang SONG is a Ph.D. candidate of Information Systems at Singapore Management University. Her general research interests are in social media and e-commerce.