[Forum SIS] Avviso di seminario :: Scarsini a DSS (Scienze Statistiche, Sapienza)

[Pierpaolo Brutti]

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 A v v i s o   d i   S e m i n a r i o
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Giovedì 23 Aprile, ore 11:00am
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Stanza 34
Dipartimento di Scienze Statistiche
Sapienza Università di Roma

MARCO SCARSINI
(LUISS Guido Carli)

terrà un seminario dal titolo

ON INFORMATION DISTORTIONS IN ONLINE RATINGS.

tutti gli interessati sono invitati a partecipare.

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Maggiori informazioni sui seminari presso il DSS sono
consultabili a quest'indirizzo: http://goo.gl/Y6OQYm

Saluti

Pierpaolo Brutti

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ABSTRACT

Consumer reviews and ratings of products and services have become
ubiquitous on the Internet. This paper analyzes, given the sequential
nature of reviews and the limited feedback of such past reviews, the
information content they communicate to future customers. We focus on
the informational setting in which customers only observe the sample
mean of past reviews, and ask if customers can recover the true
quality of the product based on the feedback they observe. We first
analyze the benchmark setting, in which customers interpret the mean
as the proxy of quality. In such a case, we show that in the long
run,the sample mean of review stabilizes and  two cases may arise.If
customers are relatively homogeneous, then social learning takes
place. If customers are sufficiently heterogeneous, then they
consistently overestimate the underlying quality of the product in the
long run. This bias stems from the selection  associated with
observing only reviews of customers who purchase. We show,
however,that if customers are sophisticated, then there exists a
simple quality inference and purchasing rule  that corrects for the
selection bias and leads to  social learning.In addition,  we show
that the cumulative consumer surplus losses scale with the square root
of the number of customers who have considered a purchase to date,
which is of the same order as when customers observe the reviews of
all preceding customers. In this framework, we also analyze the
externality of sophisticated customers on more naïve ones and quantify
the impact that manipulated reviews may have.