Leveraging Social Media in the Pharmaceutical Industry - Innovation and Marketing in the Pharmaceutical Industry - page 486

Article/work Model type Data Key indings/guidelines Key limitations

Zhang et al. ( 2011 ) VAR model Weekly number of new and

returning sellers and buyers
and total weekly commission
from a C2C website in
Europe

Contributor (seller) acquisition has the
largest inancial value because of the
strong network effects on content
consumers (buyers) and other
contributors

Does not disentangle the difference sources of
network effects such as word of mouth,
observational learning, product assortment
enrichment, direct social interaction, etc.

Ansari et al.
( 2011 )

Hierarchical Bayesian
model

A sequential network of
communications among
managers involved in new
product development
activities and online
collaborative social network
of musicians

Multiple relationships (e.g., friendship,
communications, and music downloads)
share common antecedents and exhibit
homophily and reciprocity. Ofline
proximity is relevant for all online
relationships. Artists exhibit similar
roles across relationships

Does not incorporate the dynamic formation of
network connections over time

Word of mouth and social contagion in the pharmaceutical industry
Van den Bulte and
Lilien ( 2001 )

Hazard model Medical innovation data and

journal advertising data

Contagion effects disappear when
marketing efforts are controlled for

Omits detailing efforts

Manchanda et al.
( 2008 )

Binary choice model
with duration
dependence
(equivalent to a
discrete-time
hazard model)

Physician level prescriptions,
details and samples,
aggregate DTCA expendi-
tures, and addresses of 466
physicians in Manhattan and
Indianapolis markets

The social multiplier of marketing is about
11 %

Uses geographic proximity (20-mile radius) to
infer physician-to-physician contacts.
Geographical proximity can relect other
unobservables that affect the actions of all the
agents in the location similarly

Nair et al. ( 2010 ) Linear panel data
model with ixed
effects

Physician level prescriptions,
details and samples. 290
surveys of opinion leader-
physician pairs

Peer effects are statistically signiicant and
responses to marketing activity across
nominators and opinion leaders are
asymmetric

Relies on survey data conducted by a third party. It
is unclear whether data collected are from a
representative sample or whether it is subject to
selection bias. Self-reported opinion leader-
physician relationship may not capture actual
physician-to-physician communications
(continued)