Biomedical Engineering Reference
In-Depth Information
Table 16.2 Summary of selected research on word of mouth in social media and social contagion in the pharmaceutical industry
Article/work
Model type
Data
Key indings/guidelines
Key limitations
Online word of mouth
Godes and
Mayzlin ( 2004 )
Linear panel data
model with ixed
effect
Online posts on 44 TV shows
from Usenet newsgroups
Dispersion of WOM rather than volume of
WOM is a signiicant explanatory factor
of TV rating
Ignores marketing mix variables. Dispersion and
volume of WOM are moderately correlated.
Results may not be generalizable to purchase
decisions of products with inancial costs
Bart et al. ( 2005 )
Structural equation
analysis
6,831 consumers across 25
websites from 8 website
categories
The drivers and role of online trust are
different across site categories and
consumers
Does not include spillover effects of online trust
over time
Chevalier and
Mayzlin ( 2006 )
Log-log cross-
sectional model
Sales ranks and reviews of 2,387
topic titles from Amazon.
com and bn.com
Number of reviews, length of reviews, and
review stars are signiicant predictors of
sales ranks
Treats reviews as exogenous to sales ranks. Ignores
carryover effects of reviews and sales ranks
Mayzlin ( 2006 )
Game theoretic model
Not available
Firms with lower quality products spend
more resources on promotional chat on
the Internet than irms with higher
quality products
No empirical evidence
Liu ( 2006 )
Log-log cross-
sectional model
for each week
12,136 messages that discuss 40
movies on Yahoo.com
message board
Volume of WOM rather than valence of
WOM is a signiicant predictor of box
ofice revenues
Assumes WOM can only affect box ofice revenues
in the subsequent week
Godes and
Mayzlin ( 2009 )
Aggregate market-
level model
A ield test involving 1,000
agents across 15 markets in
the United States and an
experiment with 96
responses
Firm generated WOM is more impactful if
the WOM was spread from a less loyal
customer than from a loyal customer
and to an acquaintance than to a friend
or a relative
Assumes all WOM is positive. Analysis relies on
self-reported WOM
Trusov et al.
( 2009 )
VAR model
E-mail invitations and sign-ups
to a social networking site.
Media mentions of the site
and promotional events
WOM referrals have substantially longer
carryover effects than traditional
marketing actions and produce
substantially higher response elasticities
Ignores valence of WOM. Cannot account for
heterogeneity using aggregate data. Sign-up
time-series data are stationary, suggesting none
of the studied covariates have permanent effects
on sign-ups
(continued)
 
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