Conference Sessions - Jesse H. Jones Graduate School of ...

SA11 MARKETING SCIENCE CONFERENCE – 2011
4 - From Invention to Innovation: Technology Licensing by New Ventures
in the Biopharmaceutical Industry
Yansong Hu, Assistant Professor, Warwick University,
Warwick Business School, MSM Group, Coventry, CV4 7AL,
United Kingdom, yansong.hu@wbs.ac.uk
Licensing out technologies is a major source of revenue for new technology ventures.
While technology licensing is economically important and poses unique challenges, it
has yet received little attention in past marketing literature. We analyze how alliances
with established firms, structural positions within scientific research, patent and
commercial networks, and the nature of technology portfolios (i.e., radical and
incremental innovations) of new ventures influence the number of successful
licensing deals secured by the new ventures. We draw on theories of structure and
legitimacy of new ventures to conjecture how the number and nature of alliances,
centrality positions within scientific research, patent and commercial networks, and
the composition of technology portfolios can affect a new venture’s number of
licensing deals, and how these effects may vary over time. We study these key issues
by examining all public firms in the UK biopharmaceutical industry from 1989 to
2009. Because we observe overdispersion in the data, we specify a negative binomial
model to investigate the conjectured effects as drivers of successful deals, and provide
a sharper understanding of the relations between the collaboration and co-creating
with innovation communities, structural positions within innovation networks, the
trade-off between radical and incremental innovation portfolios, and time of the
specific innovation.
■ SA11
Champions Center I
Social Influence I
Contributed Session
Chair: Jose-Domingo Mora, Assistant Professor of Marketing, University of
Massachusetts Dartmouth, 285 Old Westport Road, CCB 219, North
Darthmouth, MA, 02747, United States of America, jmora@umassd.edu
1 - The Silent Signals: Implicit User Generated Content and Implications
for Consumer Decision Making
Sunil Wattal, Assistant Professor, Temple University, 1810 N. 13th
Street, Philadelphia, PA, 19122, United States of America,
swattal@temple.edu, Anindya Ghose, Gordon Burtch
The body of work on user-generated content (UGC) has burgeoned of late. However,
much research on UGC focuses on explicit contributions (e.g. discussion boards and
online product reviews). There is a notable lack of consideration for implicit forms of
UGC in this body of work, where statements of approval or disapproval are implied
by users’ actions. Much of this work also lacks a holistic consideration of the process
by which users consume such information and render their decisions. To examine
these phenomena, we analyze panel data drawn from a crowd-funded marketplace
that enables authors to pitch their news article ideas to the crowd, and to
subsequently obtain investment from them to pursue the article’s research and
publication. We propose two novel measures of implicit UGC: investment frequency
(the number of supporters per day of funding) and density (the number of supporters
per dollar of funding). We find that users’ likelihood of investing in a project
decreases with the frequency of prior support, suggesting anti-herding or contrarian
behavior manifests in this context. Further, we find that users’ likelihood of investing
increases with density of support, implying that users are not swayed by abnormally
large contributions, perhaps because they are presumed to be contributions by the
author’s friends and family, or by the author themselves. We discuss the implications
of our findings for practitioners and scholars dealing with implicit UGC, and identify
avenues for subsequent research.
2 - A Model of Social Dependence and Intra-group Interaction
Youngju Kim, Doctoral Student, Korea University, Business Main
Building, Seoul, 136-701, Korea, Republic of, aa0124@korea.ac.kr,
Jaehwan Kim, Neeraj Arora
To better explain group decision outcomes, we attempt to capture both social
influence on each member and the intra-group interaction among members when
they are engaged in choice decision. Since various purchases are made by groups in
market place, and their choices are affected by individual member’s preference, which
is also affected by other people around each members through her/his own network
structure, capturing both effects is necessary. It is expected that ignoring these sources
will result in misinterpretation of the individual and group behaviors. Through
simulation study and empirical estimation against real data, we found that the model
neglecting either of the two influences gives rise to biases in three ways: First, the
model systematically underestimates the importance of factors driving consumer
choices. Second, the model systematically underestimates the heterogeneity among
decision units. Third, the group-level parameters, power of the group members on
their group decision, are also distorted.
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3 - You May Have Influenced My Next Purchase: Social Influence in
Food Purchase Behavior
Jayati Sinha, PhD Student, University of Iowa, S252 John Pappajohn
Business Building, Iowa City, IA, 52242-1000, United States of
America, jayati-sinha@uiowa.edu, Gary J. Russell, Dhananjay
Nayakankuppam
Social influence is an important aspect of consumer decision making. Social influence
can manifest itself in two ways: social interaction effects (interactions among
neighbors) and neighborhood effects (influence of spatially proximate others). In this
research, we argue that, beyond commonly evoked effects of the socio-physical
environment, neighborhood social interaction patterns may have a decisive influence
on food consumption behavior. Moreover, due to the nature of these social processes,
this phenomenon should be more likely to emerge for product categories that are
socially salient. We test these hypotheses by fitting a conditional autoregressive (CAR)
spatial model to purchase data collected in two product categories: organic foods
(socially salient) and non-organic meat and poultry (not socially salient). Although
both categories provide evidence for the existence of neighborhood effects, only
organic foods exhibit clear evidence that the level of social interaction drives
purchases. We discuss the implications of these results for the effectiveness of social
media in marketing. In particular, we argue that socially expressive product categories
(such as green products) are better candidates for marketing strategies incorporating
social media.
4 - Intra and Cross-household Influences as Predictors of
Individual Consumption
Jose-Domingo Mora, Assistant Professor of Marketing, University of
Massachusetts Dartmouth, 285 Old Westport Road, CCB 219,
North Dartmouth, MA, 02747, United States of America,
jmora@umassd.edu
There is clear evidence in marketing scholarship on the networked nature of the
demand side of markets. There have been few attempts though at incorporating such
influences in models predicting individual consumption. Major difficulties in this
regard are posed by data sets reporting individual behaviors, not one-on-one
interactions, and by the endogenous relationships between the amounts of
consumption of interacting individuals. We present a random coefficients model of
individual consumption where intra-household and cross-household influences are
captured by proxy variables. This model is estimated on a cross-section of peoplemeter
data reporting individual consumption of television programs. Program
consumption of wives is defined as the DV. Building on Lull (1980, 1982); Yang,
Narayan & Assael (2006) and Yang, Zhao, Erdem & Zhao (2010) intra-household
influences between wives and family members are captured as co-viewing of
television programs. Two alternate operationalizations of co-viewing are tested.
Consistent with research in social networks, levels of socio-economic status by city
are assumed as independent “cells” where social exchanges take place among wives.
The social multipliers for each of these cells, subscripted by program genre, are
estimated using Graham’s (2008) linear-in-means model of social interactions. The
estimated social multipliers enter the RCM of individual consumption of wives as
proxy variables for cross-household influences. A non-hierarchical specification of
this RCM is compared with a hierarchical specification where social influences
mediate the effects of demographics and program characteristics.
■ SA12
Champions Center II
Online Word of Mouth Research
Contributed Session
Chair: Mounir Kehal, Research Faculty and Professor of BIS and
Computing, ESC Rennes School of Business, 2, rue Robert d’Arbrissel, CS
76522 Cedex, Rennes, 35065, France, mounir.kehal@esc-rennes.fr
1 - Get Something for Nothing: Designing Optimal Free Sampling
Strategy for Online Communities
Shuojia Guo, Rutgers University, 1 Washington Park, Newark, NJ,
07102, United States of America, nancygsj@gmail.com, Lei Wang,
Yao Zhao
Firms often offer free samples to attract customers. Free samples may affect sales in
three ways. First, the sampling event itself raises product and brand awareness among
customers. Second, the samples reveal true product quality and attract repeated
purchases from satisfied customers. Third, customers who have the chance to try the
free samples may spread positive or negative word-of-mouth to other potential
customers. In this paper, we empirically investigate the causal effect of a free
sampling event on a product’s sales. Specifically, we decompose the sampling effect
into three effects: awareness effect, experience effect, and word-of-mouth effect.
Using data from the largest Chinese e-commerce website taobao.com, we identify
factors that affect these three effects and discuss optimal sampling strategies in terms
of optimal size of free samples and optimal product pick for multi-product retailers.