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The Economic Value of Online Reviews
by
Lu, Xianghua
, Wu, Chunhua
, Chan, Tat Y.
, Che, Hai
in
Cognitive models
/ Consumer behavior
/ consumer choice under uncertainty
/ Consumer preferences
/ Consumers
/ Customer feedback
/ Dianping.com
/ Economic aspects
/ economic value to consumer and firm
/ Evaluation
/ Experiments
/ Forecasts and trends
/ Internet
/ learning
/ online reviews
/ Population distribution
/ Restaurants
/ Studies
/ User generated content
/ Value
/ Value chain
/ Websites
2015
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The Economic Value of Online Reviews
by
Lu, Xianghua
, Wu, Chunhua
, Chan, Tat Y.
, Che, Hai
in
Cognitive models
/ Consumer behavior
/ consumer choice under uncertainty
/ Consumer preferences
/ Consumers
/ Customer feedback
/ Dianping.com
/ Economic aspects
/ economic value to consumer and firm
/ Evaluation
/ Experiments
/ Forecasts and trends
/ Internet
/ learning
/ online reviews
/ Population distribution
/ Restaurants
/ Studies
/ User generated content
/ Value
/ Value chain
/ Websites
2015
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Do you wish to request the book?
The Economic Value of Online Reviews
by
Lu, Xianghua
, Wu, Chunhua
, Chan, Tat Y.
, Che, Hai
in
Cognitive models
/ Consumer behavior
/ consumer choice under uncertainty
/ Consumer preferences
/ Consumers
/ Customer feedback
/ Dianping.com
/ Economic aspects
/ economic value to consumer and firm
/ Evaluation
/ Experiments
/ Forecasts and trends
/ Internet
/ learning
/ online reviews
/ Population distribution
/ Restaurants
/ Studies
/ User generated content
/ Value
/ Value chain
/ Websites
2015
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Journal Article
The Economic Value of Online Reviews
2015
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Overview
This paper investigates the economic value of online reviews for consumers and restaurants. We use a data set from
Dianping.com
, a leading Chinese website providing user-generated reviews, to study how consumers learn, from reading online reviews, the quality and cost of restaurant dining. We propose a learning model with three novel features: (1) different reviews offer different informational value to different types of consumers; (2) consumers learn their own preferences, and not the distribution of preferences among the entire population, for multiple product attributes; and (3) consumers update not only the expectation but also the variance of their preferences. Based on estimation results, we conduct a series of counterfactual experiments and find that the value from Dianping is about 7 CNY for each user, and about 8.6 CNY from each user for the reviewed restaurants in this study. The majority of the value comes from reviews on restaurant quality, and contextual comments are more valuable than numerical ratings in reviews.
Publisher
INFORMS,Institute for Operations Research and the Management Sciences
Subject
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