题　目：Not So Timely: Push-Notification Timing and User Engagement

嘉　宾：钟泽民 副教授 多伦多大学罗特曼管理学院

主持人：左思 助理教授 上海交通大学安泰经济与管理学院

时　间：2026年5月20日（周三）16:30-18:00

地　点：上海交通大学 徐汇校区 安泰经济与管理学院A303

内容简介:

The availability of granular user data allows digital platforms to time interventions with remarkable precision. However, does delivering messages exactly when a user is active truly maximize consumer response? We leverage a novel dataset from a leading push notification provider in China that captures the precise timestamps of screen-on events, inferred from mobile device app usage, and notification delivery. We exploit a quasi-experiment where a user's screen-on timing is effectively random relative to the push request within a short window, creating exogenous variation in the timing gap between screen-on and delivery. Regression discontinuity estimates reveal that, contrary to industry wisdom, immediate screen-on delivery backfires, reducing same-day app logins by approximately 16%. To isolate the effects of different lags, we instrument the realized delay using granular variations in screen-on timing. We find a non-monotonic pattern: users receiving notifications with a modest delay (1-6 minutes) after a screen-on event exhibit peak engagement, surpassing both instant delivery and longer lags. Heterogeneity analysis shows that this penalty for immediate interruption is most pronounced during high-stakes periods, such as weekday mornings. Suggestive evidence from a second live-streaming app and from longer-term outcomes (app uninstalls) is consistent with the main findings. These findings challenge the race for immediacy, suggesting that a modest delay balances intrusiveness against timeliness, making it a variable to optimize rather than an inefficiency to eliminate.

演讲人简介：

Zemin (Zachary) Zhong is an Associate Professor of Marketing at the Rotman School of Management, University of Toronto, currently visiting CityU while on sabbatical. He earned his Ph.D. from Berkeley-Haas. His research spans substantive topics in digital marketing, including LLM-based AI shopping assistants, algorithmic targeting, platform search, review system, and push notifications, often utilizing data from industry partners like Alibaba and Ctrip. He serves as an Associate Editor of Marketing Science.

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