Designing AI-Generated Summaries for Online Video Platforms: Evidence from a Field Experiment
June 11, 2025 MGT
| Topic: | Designing AI-Generated Summaries for Online Video Platforms: Evidence from a Field Experiment |
| Time&Date: | 10:30 am -12:00 pm, June 11, 2025 (Wednesday) |
| Venue | Room 604, Teaching Complex D Building |
| Speaker: | Chaoyue Gao University of Science and Technology of China |
| Abstract: | AI-generated summaries condense lengthy text or video content into short and concise textual summaries, which can facilitate consumer search and reduce information overload. This research designs and evaluates two AI-generated summarization strategies for online videos: information-extractive AI-generated summaries (IAIGS), which present fact-based recaps, and suspense-inducing AI-generated summaries (SAIGS), which withhold key details to spark curiosity. To assess their impact on video consumption, we conducted a randomized field experiment on a video-sharing platform, focusing on two content genres: Science, which primarily addresses users’ instrumental information needs, and Humanities & History, which caters to users’ affective and curiosity-driven interests. We analyzed engagement metrics for 21,533 videos from 1,545 content creators over a six-week period. Our results show that IAIGS consistently reduced video views across both genres, driven by information substitution. In contrast, SAIGS had genre-specific effects: it increased engagement with Humanities & History content by sparking curiosity, but decreased engagement with Science content by obstructing information-seeking. A follow-up online experiment confirmed these patterns and shed light on the underlying mechanisms. Our study highlights the nuanced effects of AI summarization strategies on user engagement across content genres, emphasizing the importance of aligning summary design with user intent and content characteristics. Our findings provide valuable insights for designing AI-generated summaries to enhance user engagement and content consumption. |
| Biography: | Dr. Chaoyue Gao is a Tenure-track Associate Professor in the School of Management at the University of Science and Technology of China, specializing in Information Systems. He earned his Ph.D. in Management from Harbin Institute of Technology and also hold a Ph.D. from Department of Information Systems in City University of Hong Kong. In addition, he possess a B.E. and a B.S. in Management, an M.E., and a minor in Management from HIT. His research primarily investigates the practical applications of blockchain technology and Large Language Models (LLMs), focusing on their influence on user behavior. His methodological approach is rooted in econometrics, utilizing secondary data for research, complemented by lab or field experiments and design science methods. His research has been published in various leading journals such as Journal of Operations Management, Production and Operations Management, Decision Support Systems and Management World. He has received multiple Best Paper Awards and Best Paper Nominations (ICIS 2024, AIS SIGBIT 2024, CSWIM 2022, CSWIM 2023, PACIS 2023, POMS International Conference in China 2024). |
