题　目：Learning when Reading: Evidence from an Online Mobile Reading Platform

嘉　宾：Yuchen Liu, PhD candidate, University of Washington

主持人：魏煊  助理教授  上海交通大学安泰经济与管理学院

时　间：2023年11月15日（周三）09:30-11:00

地　点：腾讯会议

(校内师生如需会议号和密码，请发送电邮至xuziqing@sjtu.edu.cn获取)

内容简介:

Online reading platforms offer a by-chapter purchase method and implement a novel in-chapter online comment function to mitigate consumer uncertainty regarding the true quality of ebooks. In this study, we seek to uncover how such intervention affects consumer purchase decisions. In particular, we implement a Bayesian learning framework to unveil the consumer learning process enabled by such a by-chapter purchase method, incorporating both the consumer’s direct reading experience and previous readers’ indirect experience reflected by inchapter comments as quality signals. Our model estimation results show that consumers learn the quality of books across different genres from their direct experience at a different pace.

Moreover, our analysis reveals that the in-consumption comments of different topics regarding their content have heterogeneous signaling effects on book quality. Consumers facing more comments that comprise a neutral or emotional discussion over the plot and those begging the author to post new chapters would revise upward their perception of the book quality. Finally, we discover that consumers approaching the end of a book or exposed to a list of chapters with few informative names tend to skip more chapters instead of reading continuously.

演讲人简介：

Yuchen Liu is a PhD candidate in information systems at the Foster School of Business, University of Washington, Seattle. Yuchen’s research centers on the economics of digital consumption on community-based online platforms, from two perspectives. Along the first perspective, she leverages structural modeling techniques to examine the impacts of various IT-enabled platform designs (e.g., in-consumption communication function and motivation mechanisms) on users’ intertemporal behaviors and their underlying decision-making processes. Simultaneously, along the second perspective, her work extends to studying how the spread of information among these users is affected by information attributes (e.g., emotional intensity) as well as platform interventions (e.g., social media misinformation countermeasures). Yuchen applies econometric modeling and machine learning techniques to tackle the questions in these fields. Several of her papers are going through major revision at Information Systems Research and she has received best student paper nomination at the field’s top conference (e.g., CIST).

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