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学术讲座

“Only the Interested Learn” – Proactive Learning of Product Reviews

时间:2018-04-06

Marketing Seminar2018-02


Topic: “Only the Interested Learn” –Proactive Learning of Product Reviews

Speaker: Liye Ma, Associate Professor of Marketing, the University of Maryland, College Park

Time: Wednesday, 11 April, 13:30-15:00

Location: Room 217, Guanghua Building 2


Abstract:

We develop a sequential learning model to analyze how consumers proactively acquire product quality information through reading online product reviews. Product reviews are often treated as exogenous quality signals in the Internet word-of-mouth literature. Review reading, however, is a costly activity undertaken in a deliberate manner. Our model casts consumers’ review reading process in a rational framework. In our model, whether a consumer reads reviews, and of which products, depend on the consumer’s information set and expectations. We estimate the model using a rich dataset from a restaurant review website which contains information on both browsing and purchase. We find strong evidence of consumers consciously seeking product reviews, and parameter estimates reveal distinct types of information acquisition behaviors. Comparison with alternative models shows that taking reviews as exogenous signals leads to biased estimates on quality levels and signal precisions. Counterfactual analysis further shows that the reviews a consumer sees earlier is more consequential, due partly to their impact on subsequent search actions, and that sorting reviews from lowest to highest ratings leads to highest differentiation across products. Our study is the first to analyze consumer’s review reading process and to analyze the effect of reviews in this dynamic context.


Introduction:

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Liye Ma, Associate Professor of Marketing, Robert H. Smith School of Business, University of Maryland.

He is interested in Dynamic and Interactive Consumer Decisions, Internet/ Social Media Marketing, Mobile Marketing, Choice Models, Theory-Driven Econometric Models, Big Data, Machine Learning.


Your participation is warmly welcomed!

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