Are Ratings Informative Signals? The Analysis of The Netflix Data
AbstractThe aim of this research is to analyze whether and when ratings are informative signals about the quality of movies. The ratings data of Netflix is used to fit a structural Bayesian learning model. This model links revealed experience utilities of raters, previous consumers, to the product choice of the future consumers of the same good. I postulate that movies are chosen based on the prior beliefs' and signals' precisions. The extent of signals' use depends on their informativeness, that is on how many consumers revealed their preferences before. The results demonstrate that consumers learn about the quality using ratings as signals. The signal produced by one rating is very noisy and might not be taken into account. The more people rate, the better are signals' quality. Consumers are not considerably dispersed in how they value quality.
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Bibliographic InfoPaper provided by NET Institute in its series Working Papers with number 08-22.
Length: 35 pages
Date of creation: Sep 2008
Date of revision: Oct 2008
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Web page: http://www.NETinst.org/
rating; quality; learning; motion pictures;
Find related papers by JEL classification:
- L15 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Information and Product Quality
- L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce
- L82 - Industrial Organization - - Industry Studies: Services - - - Entertainment; Media
- L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
- D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
- C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-10-21 (All new papers)
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