题　目：Time Cost Illusions: Purchase-Usage Gap in Online Education 

嘉　宾：Yulin Hao (郝宇琳), Ph.D. Candidate, University of Rochester

主持人：张铄 副教授 上海交通大学安泰经济与管理学院

时　间：2025年11月5日（周三）9:00-10:30

地　点：上海交通大学 徐汇校区 安泰经济与管理学院A511

内容简介:

Using proprietary data containing complete consumer purchase and usage records from a large farmer education platform in China, we document a substantial purchase–usage gap: on average, consumers complete only 30% of the content they buy, despite an average course price of $74. We trace this gap to a time cost illusion: consumers underestimate the time cost of learning at purchase and thus choose courses that demand more effort than they can commit. More importantly, marketing messages that downplay effort amplify this bias. Over successive purchases, consumers partially correct the bias by shifting toward shorter, easier courses and becoming less susceptible to marketing messages. We build a structural model of purchase and usage in which marketing messages affect perceived time cost. Estimates imply that, at purchase, perceived cost is only one-third of the true cost, and that marketing-induced illusion reduces consumer surplus by $48 per enrollment. Disabling effort-downplaying marketing messages lowers purchases by 12 percentage points but raises usage by 21 percent, as consumers self-select into lower-cost courses. Counterfactual simulations show that either (i) splitting long courses into shorter modules or (ii) disclosing historical completion rate reduces the purchase–usage gap by aligning ex-ante expectations with ex-post usage.

演讲人简介：

Yulin Hao is a Ph.D. candidate in Quantitative Marketing at the Simon Business School, University of Rochester. His research spans two broad areas: (i) hidden frictions in consumer journeys (e.g., information and search frictions) and (ii) peer networks and effects (e.g., influencer agencies, sales teams). Most of his work involves collaborations with industry partners in emerging markets. Methodologically, he employs structural modeling, causal inference, and machine learning, complemented by surveys and field experiments.

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