Bachelor Thesis - The Role of Seeding Strategy
Transcript of Bachelor Thesis - The Role of Seeding Strategy
The Role of Hubs in Seeding Strategies
Bachelors Thesis
Chair of Quantitative Marketing Prof. Dr. Florian Stahl
Advisor:
Andreas Lanz
University of Mannheim Spring Term 2015
Weiquan Alvin Liu Matriculation Nr. 1394127 B.Sc. Betriebswirtschaftslehre Apt. 112, Carl-Metz-Str. 2, 68163 Mannheim (+49) 170 4184004 [email protected]
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Table of Content
Abstract II
1. Introduction 1
2. Theoretical Framework 3 2.1 Innovation Diffusion 3 2.2 Social Networks and Viral Marketing 4 2.3 Types of Seeding Strategies 6 2.4 Concept of the Influentials 7
3. Effects of Hub Seeding Strategies 9 3.1 Superior Influence of Hubs 9 3.2 Acceleration of Adoption 13 3.3 Expansion of Market Size 17
4. Discussion 21 4.1 Critical Evaluation 21 4.2 Managerial Implications 23 4.3 Future Research 24
References 25
Appendix 29
Affidavit 37
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Abstract
The success of social media platforms throughout the world has prompted many
marketing practitioners to increasingly shift their word-of-mouth communications online in
order to take advantage of the speed and potency of online viral marketing. Many people tend
to a priori expect highly connected individuals to be more effective initial seeding targets
because it seems logical that a message can become contagious more easily through their
large amount of connections. Hence, this thesis seeks to better understand the different roles
of hubs in seeding strategies by reviewing some recent relevant literature. More specifically,
three prominent effects of hub seeding strategies will be discussed to provide more insight
into how hubs can contribute to successful online viral marketing.
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1. Introduction
In recent years, social media platforms such as Facebook, Twitter, and YouTube have
experienced rapid and immense growth due to the seemingly ever-increasing number of
Internet users. The development of low-cost broadband and mobile networks has enabled
people who are living in some of the world’s most impoverished or rural regions to have
access to the Internet, allowing them not only to exchange knowledge with the rest of the
world but to also be able to quickly spread information among their local communities. This
unprecedented freedom of communication has a profound impact on the dynamics of social
networks and has resulted in the surge of online campaigning. A notable example is the
critical role of social media platforms in the Arab Spring, where according to a survey
conducted by the Dubai School of Government, more than 88% of Egyptians and Tunisians
obtained their information on the civil movement through popular social media sources such
as Facebook and Twitter (Mourtada and Salem 2011, p. 8).
Such online campaigning is not only limited to the developing world. The Occupy
movement, which initially began as a protest against economic inequality and political
corruption in New York City’s Wall Street financial district, has also made use of Facebook
and Twitter to disseminate information and amass support across hundreds of cities in the
USA and Europe. Although the movement’s physical manifestations were largely sporadic
and short-lived, its viral online presence continues to grow exponentially and evolve into
various subgroups, enabling demonstrations to more persistently and spontaneously recur like
in the recent case of Blockupy Frankfurt.
The potential of a social network to propagate information and influence its actors
using word-of-mouth has been amplified by the tremendous increase in social media
penetration around the world. The mobility and ease of sharing information brought about by
social media technologies meant that an idea or innovation introduced into a social network
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could, under the right circumstances, very quickly achieve self-sustained propagation and
become viral within the network.
The prospect of being able to systematically induce such a self-replicating process is
invaluable to marketing, especially in the business context. Companies could leverage on
social media technologies to increase sales or spread awareness about a new brand or product
without having to resort to costly traditional mass media. Viral marketing seems to promise
the best of both worlds – on one side, it only requires relatively little resources to seed a small
group of people, but on the other side, it could very quickly generate an extensive reach by
harnessing the effects of word-of-mouth within online social networks.
This thesis seeks therefore to identify the different types of seeding strategies
commonly used and understand how seeding social hubs will affect the diffusion process and
the ultimate success of a viral marketing campaign. The thesis starts off broadly by building
the theoretical framework based on innovation diffusion, before introducing concepts
regarding social network and viral marketing. Thereafter, the different types of seeding
strategies as well as the concept of influentials and hubs will be clarified.
In the main part of the thesis, emphasis is placed on three of the most widely
researched and discussed effects of hub seeding due to their significance to viral marketing
success. First, we will explore whether hubs are actually more influential than others, and if
so, what some of the reasons for this superior influence are. Second, we will look into how
seeding hubs can lead to the acceleration of the adoption and its economic benefits. Third, we
will examine how seeding hubs can lead to the expansion of the market size and how its
resulting economic benefits compare with that of accelerated adoption.
Finally, the reviewed literature is critically evaluated, and the managerial implications
of its findings will be discussed. The future research opportunities for the role of hubs in
seeding strategies and viral marketing will also be presented.
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2. Theoretical Framework
2.1 Innovation Diffusion
In order to understand the mechanisms of viral marketing, we first have to look at the
fundamental theory of diffusion and the underlying influences that drive the spread and
adoption of new innovations. The term innovation diffusion is defined as “the process of the
market penetration of new products and services, which is driven by social influences” (Peres,
Muller, and Mahajan 2010, p. 92).
Most of diffusion modeling has been done based on the Bass model framework (Peres,
Muller, and Mahajan 2010, p. 91), which classifies adopters into either innovators or imitators
according to the timing of their adoption (Bass 1969, p. 216). Innovators are pioneers who
make adoption decisions independently of the decisions of others, while imitators adopt under
the increasing pressure and influence of others in the social system (Bass 1969, p. 216). The
probability of an individual’s adoption is then presented to be linear with respect to the
number of previous adopters (Bass 1969, p. 226). More recently, Van den Bulte and Joshi
(2007, p. 400) also presented an asymmetric influence model, dividing potential adopters into
two segments, where one segment can affect another segment, but not vice versa.
The Bass model states that adoption happens due to two types of influences: external
influences, such as advertisements and communications conducted by the company, and
internal market influences that arise from social interactions between adopters and potential
adopters (Peres, Muller, and Mahajan 2010, p. 91).
Traditionally, internal market influence is interpreted as the effect of word-of-mouth
communication among individuals (Peres, Muller, and Mahajan 2010, p. 92). Word-of-mouth
is defined by Iyengar, Van den Bulte, and Valente (2008, p. 91) as the achievement of social
contagion through oral or written communication, where people’s behavior is influenced by
the exposure to knowledge, attitudes, or behavior of others. However, the Bass model has not
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specifically defined or restricted the drivers of social contagion (Peres, Muller, and Mahajan
2010, p. 92). Thus, more recent studies have expanded the scope to include all other kinds of
social interdependence such as observational learning, normative pressures, and competitive
concern (Van den Bulte and Lilien 2001, p. 1410).
Van den Bulte and Lilien’s (2001, p. 1429) statistical analysis suggests that in earlier
literature, social contagion’s influence on innovation diffusion may be confounded with the
effect of marketing effort. However, more recent studies that control for such marketing effort
and other potential confounds continue to confirm the existence of social contagion in new
product adoption (Iyengar, Van den Bulte, and Lee 2015, p. 18; Iyengar, Van den Bulte, and
Valente 2011, p. 196). In addition to these studies, the decreasing effectiveness of traditional
mass media advertising and recent development of social media technologies have prompted
companies to invest more marketing resources to strengthen their internal market influences
directly through social networks (Peres, Muller, and Mahajan 2010, p. 93).
2.2 Social Networks and Viral Marketing
The term social network is defined as “a set of actors and the relationships among
them” (Goldenberg et al. 2009, p. 2), while the term viral marketing is used to describe
marketing campaigns that are deliberately planned to capitalize on the effects of word-of-
mouth in order to induce social contagion (Iyengar, Van den Bulte, and Valente 2008, p. 91).
In the past, companies have often made use of unsolicited e-mails as an electronic
form of word-of-mouth communication (De Bruyn and Lilien 2008, p. 152). However, due to
the widespread use of spam filters, companies have been increasingly shifting their resources
to social media marketing activities (Hinz et al. 2011, p. 55) in order to reach large amounts
of people in a relatively short period of time (Van der Lans et al. 2010, p. 348). Viral
marketing campaigns are also significantly cheaper as compared to traditional advertising
because the burden of spreading marketing-relevant information in the social network is
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transferred to self-motivated customers (Hinz et al. 2011, pg. 55).
Van der Lans et al. (2010, p. 349) state that there are two main strategies to viral
marketing. One aims to motivate customers to spread marketing-relevant information by
using intrinsic incentives, which can be sparked by the content of message, or extrinsic
incentives such as rewards and monetary prizes (Godes et al. 2005, pg. 419). The other seeks
to control the entire initiation process by predetermining the number of seeded customers,
their social positions, and the seeding medium (Van der Lans et al. 2010, p. 349).
Hinz et al. (2011, p. 56) have proposed a four-determinant model to measure the
extent of social contagion induced and determine the success of viral marketing campaigns.
This is done by calculating the expected successful referrals SR of an individual i, who
becomes informed of the marketing message sent to him or her with information probability Ii
(Hinz et al. 2011, p. 56). Individual i could then actively take part in the spread of the
message with participation probability Pi and forward the message to a selected ni number of
people with his or her used reach (Hinz et al. 2011, p. 56). Assuming the individual i has the
same conversion rate wi for all ni number of people (Hinz et al. 2011, p. 56), then the number
of successful referrals can be presented as stated by Hinz et al. (2011, p. 56):
SR! = I!!×!P!!×!n!!×!w!
To launch an effective and contagious viral marketing campaign, companies also need
to pay attention to 4 important factors (Hinz et al. 2011, p. 55). First, the content of the
message must be attractive and interesting so that people will remember it and pass it on
(Berger and Milkman 2012, p. 201; Berger and Schwartz 2011, p. 877). Second, some types
of social network structure such as the scale-free network are more efficient and suitable for
message propagation (Bampo et al. 2008, p. 286). Third, certain behavioral characteristics and
incentive systems can encourage people to share the message (Libai et al. 2010, p. 270).
Fourth, the type of seeding strategy used, which determines the initial set of recipients of the
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marketing message, can directly influence the speed at which the message spreads in the
social network, the eventual size of the market (Goldenberg et al. 2009, p. 10; Libai, Muller,
and Peres 2013, p. 162), the number of successful referrals, and consequently, the economic
success of the viral marketing campaign (Bampo et al. 2008, p. 287; Hinz et al. 2011, p. 68;
Iyengar, Van den Bulte, and Valente 2011, p. 169).
2.3 Types of Seeding Strategies
There are four main types of seeding strategies that have been widely researched.
First, the random seeding strategy selects the initial recipients randomly and assumes no
relationship between an individual’s social position and the determinants of social contagion
(Hinz et al. 2011, p. 59). This strategy is used as a reference point to compare with situations,
in which no data about the social network is given (Hinz et al. 2011, p. 59).
Second, the bridge or high-betweenness seeding strategy selects initial recipients who
are located between otherwise disconnected parts of the network (Hinz et al. 2011, p. 59). The
sociometric measure of betweenness centrality indicates the extent to which an individual acts
as an intermediary between different parts of a network (Hinz et al. 2011, p. 56). Seeding
individuals with high betweenness centrality can prevent the message from only circulating in
highly dense parts of the network that are already infected and allow it to spread further
throughout the entire network (Granovetter 1973, p. 1369; Hinz et al. 2011, p. 59).
Third, the fringe seeding strategy or low-degree seeding strategy chooses poorly
connected individuals, who are usually located at the fringes of the network as initial
recipients (Hinz et al. 2011, p. 56). The sociometric measure of degree centrality describes the
extent to which an individual is connected within his or her local environment (Hinz et al.
2011, p. 56). Using computer simulation modeling of social influence processes, Watts and
Dodds (2007, p. 454) show that in most circumstances, large-scale cascades of influence are
driven by a critical amount of easily influenced individuals, presumably located at the fringes
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of the network with low degree centrality, rather than particularly influential individuals. This
is further supported by Galeotti and Goyal (2009, p. 521), who argue that companies should
seed individuals with fewer connections if the probability of adoption is positively related
with the absolute number of adopting connections.
From the opposite perspective, Porter and Donthu (2008) suggest that because
individuals with high degree centrality are exposed to large amount of connections and
information, they suffer from information overload and are therefore comparatively harder to
influence. In addition, Leskovec, Adamic, and Huberman (2007, p. 37) as well as Katona,
Zubcsek, and Sarvary (2011, p. 426) find that although these individuals send out more
referrals than others, their average success rate declines after a certain number of referrals,
implying that people only have influence over a few friends, but not everyone they know.
Fourth, the hub seeding strategy or high-degree seeding strategy targets well-
connected individuals, who are centrally located in their parts of the network (Bampo et al.
2008, p. 277; Hinz et al. 2011, p. 56). There has been a lot of research attention on the roles of
hubs in the diffusion process (Peres, Muller, and Mahajan 2010 p. 93). We will go deeper into
the three most prominent effects of seeding hubs in the main part of the thesis.
2.4 Concept of the Influentials
Before we look more specifically into these effects, it is important that we examine the
concept of a special group of people, called “the Influentials”, often used in the marketing
literature and identify the various characteristics that further categorize them into subgroups
such as opinion leaders, market mavens, and hubs (Goldenberg et al. 2009, p. 1).
Weimann (1991, p. 276) finds that influential people have a combination of three
social and personal characteristics: (1) the personification of some values (“who one is”), (2)
competence (“what one knows”), and (3) social location (“who one knows”). Accordingly,
Goldenberg et al. (2009, p. 1) also identify three traits of influential people: (1) they are
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convincing, (2) they know a lot, and (3) they have a large number of social connections.
Opinion leaders are generally associated with the second attribute in that they are
more knowledgeable about the particular product category due to their enduring involvement
with it (Richins and Root-Shaffer 1988, p. 35; Venkatraman 1990, p. 60). They frequently
offer advice that is important to others with regards to product features and technical
information (Goldenberg et al. 2009, p. 2). They are not to be confused with market mavens,
who are also associated with the second attribute (Goldenberg et al. 2009, p. 2) because they
have more general knowledge about the marketplace (Feick and Price 1987, p. 83).
In contrast, hubs are associated with the third attribute in that they have very large
number of social connections (Goldenberg et al. 2009, p. 3). As mentioned earlier, the number
of connections an individual has, often termed as the “degree of a node” (Goldenberg et al.
2009, p. 3), is used to determine his or her degree centrality (Hinz et al. 2011, p. 56) in the
network. Since the distribution of people’s degrees follows a power law, only a few
individuals with the highest degree can be considered as hubs (Bampo et al. 2009, p. 227;
Goldenberg et al. 2009, p. 3).
The degree of hubs is further categorized into in- and out-degree, according to the
direction of information flow between the hub and his or her connections (Goldenberg et al.
2009, p. 4). While in-degree shows the number of connections who convey information to the
hub, out-degree indicates the number of connections whom the hub sends information to.
(Goldenberg et al. 2009, p. 4). Similar to Van den Bulte and Joshi’s (2007, p. 400) model of
asymmetric influence model, we also distinguish between innovator and follower hubs, since
there is no a priori reason or empirical evidence to associate social connectivity with personal
innovativeness (Goldenberg et al. 2009, p. 4). Therefore, while innovator hubs may adopt
earlier because they are genuinely innovative, the adoption timing of follower hubs will be
dependent on their level of exposure to other early adopters (Goldenberg at al. 2009, p. 4).
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3. Effects of Hub Seeding Strategies
3.1 Superior Influence of Hubs
Many companies base their social network marketing decisions on the assumption that
the adoption behavior of certain customers has a larger influence on the adoption behavior of
others (Iyengar, Van den Bulte, and Valente 2011, p. 195). However, if companies want to be
able to employ more effective seeding strategies in their viral marketing campaigns, they will
first have to understand the underlying drivers of this superior influence, before they can
identify and select individuals to seed. Therefore, we will review some of the recent studies
done to answer the questions: Are hubs actually more influential than others? And if that is
the case, what are the reasons for their superior influence?
As established earlier, although some research indicates that the effects of social
contagion may be inflated as a result of marketing effort (Van den Bulte and Lilien 2001, p.
1429), Iyengar, Van den Bulte, and Valente’s (2011, p. 210) study on the adoption of a new
prescription drug by physicians has provided very strong empirical evidence of social
contagion operating over network connections and affecting adoption, even after controlling
for marketing effort and other common shocks. This study combines various individual and
network-level data sets, such that the operation and dynamics of social contagion in a real
market may be observed and investigated, even when traditional marketing efforts are being
used simultaneously (Iyengar, Van den Bulte, and Valente 2011, p. 195). More specifically,
their results indicate that hubs, which are being referred to in the study as sociometric opinion
leaders, have a significantly higher correlation to adoption than self-reported opinion leaders,
supporting the hypothesis that hubs are more influential than others and could potentially
generate more successful referrals (Iyengar, Van den Bulte, and Valente 2011, p. 207).
Furthermore, the study found this superior influence to be largely driven by the high
usage volume of hubs rather than their adoption or social status (Iyengar, Van den Bulte, and
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Valente 2011, p. 210). Since usage volume does not correlate with persuasiveness, the study
suggests that high volume usage may possibly endorse potential adopters’ observational
learning about positive post-adoption outcomes as heavy users of a product tend to be more
satisfied with its performance (Iyengar, Van den Bulte, and Valente 2011, p. 210).
A more recent extension to this study conducted by Iyengar, Van den Bulte, and Lee
(2015, p. 18) using the same data set confirms that physicians with high degree centrality and
prescription volume are more influential in the trial or adoption stage because they reduce the
significant perceived risk involved in trying out a new prescription drug.
These findings also complement Godes and Mayzlin’s (2009, p. 722) study using data
collected from a large-scale company-created word-of-mouth field test and a follow-up online
experiment, even though it empirically demonstrates that light users are more effective than
heavy users at driving the spread of information at the initial awareness level. This is because
spreading a product’s awareness will only result in adoption if its level of perceived risk is so
low that little or no further information will be required in the evaluation stage (Iyengar, Van
den Bulte, and Valente 2011, p. 198). In contrast, products with significant perceived risk,
such as new prescription drugs, will require contagion to operate at the evaluation level in
order to trigger adoption (Iyengar, Van den Bulte, and Valente 2011, p. 210).
Godes and Mayzlin (2009, p. 737) further explain that because heavy users would
already have attempted to influence all other members in their social network, light users
would be more effective seeding points than heavy users. This explanation also ties in with
Iyengar, Van den Bulte, and Valente’s (2011, p. 198) suggestion that heavy users inherently
signal and influence others to adopt through observational learning.
Similarly, Hinz et al. (2011, p. 68) find hubs to be more influential in all of their three
empirical comparison studies, where the hub seeding strategy produced up to eight times
more successful referrals SRi than bridge, random, and fringe seeding strategies. The studies
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also assume information probability Ii = 1, which means that all receivers of the viral
marketing message also become aware of them (Hintz at al. 2011, p. 58).
The first study was conducted in a small and artificially bounded online social
network that consists of 120 identifiable members who were controlled for their participation
probability Pi (Hinz et al. 2011, p. 60). In this setting, where social contagion operates mainly
at the initial awareness through the extrinsic motivation of monetary rewards, hub and bridge
seeding produced between 39% to 53% more successful referrals SRi than random seeding
and between 600% to 700% more than fringe seeding (Hinz et al. 2011, p. 61).
The second study was conducted in a medium-sized online social network of 1380
unidentifiable members within a natural boundary, where social contagion also operates at the
initial awareness level but this time through the intrinsic motivation of a funny video (Hinz et
al. 2011, p. 60). The results in this more realistic setting match that of the first study, with hub
and bridge seeding strategies outperforming random seeding strategy by +60% and fringe
seeding by a factor of 3 (Hinz et al. 2011, p. 63).
The third study was conducted in a large real-world network based on a mobile phone
service provider’s data of more than 200,000 customers, where social contagion involves
belief updating at the evaluation level and referrals made to non-customers through the
extrinsic motivation of bonus airtime (Hinz et al. 2011, p. 60). Since the provider tracked all
referrals, Hinz et al. (2011, p. 63) were also able to analyze the economic outcome of different
seeding strategies and their influence on the four underlying determinants of social contagion
as shown in the first part of the thesis. However, due to the lack of information about the
relationships of non-customers, they were unable to to measure betweenness centrality, and
therefore unable to include bridge seeding strategy in this study (Hinz et al. 2011, p. 64).
The results clearly show that hub seeding has produced a larger number of referrals,
given that its participation rate Pi and used reach ni are significantly higher than that of fringe
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and random seeding (Hinz et al. 2011, p. 66). However, the study’s Poisson regression model
indicates that hub seeding is not related with its comparatively higher conversion rate wi
(Hinz et al. 2011, p. 66). Nonetheless, the overall effect of hub seeding remains to be positive,
since the number of its successful referrals SRi still surpasses random seeding by a factor of 2
and fringe seeding by a factor of 8 to 9 (Hinz et al. 2011, p. 66).
This result implies that the superior influence of hubs lies not in their higher
conversion rate wi or persuasiveness but rather in their increased levels of active participation
Pi and referral ni (Hinz et al. 2011, p. 68), which corroborates not only with Iyengar, Van den
Bulte, and Valente’s (2011, p. 210) findings about heavy users, but also with the suggestion
that as long as social contagion is only required to operate at the awareness level, the possible
higher persuasiveness of hubs becomes irrelevant (Godes and Mayzlin 2009, p. 737; Hinz et
al. 2011, p. 68; Iyengar, Van den Bulte, and Valente 2011, p. 210).
Another possible reason for the superior influence of hubs could be their high levels of
out-degree as suggested by Kiss and Bichler (2008, p. 235). Using the call data from a
telecom company and computational experiments, they compared the different centrality
measures for their ability to disseminate messages (Kiss and Bichler 2008, p. 233). They
found that the out-degree centrality measure reached a significantly higher number of
customers as compared to other centrality measures such as in-degree and betweenness (Kiss
and Bichler 2008, p. 247). This finding is in line with Goldenberg et al.’s (2009, p. 8) study,
which empirically demonstrates that the out-degree of hubs is, ceteris paribus, positively
related with their ability to influence others and drive adoption.
However, this finding only provides a limited explanation as to how the out-degree of
hubs may affect adoption, since the adoption of some riskier products requires contagion to
operate beyond the awareness level (Kiss and Bichler 2008, p. 246). Nevertheless, this further
strengthens the suggestion that persuasiveness of hubs is irrelevant if social contagion can
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occur simply through information transfer (Hinz et al. 2011, p. 68).
In summary, the presented literature shows that hubs are only more influential for the
adoption of risky products, which requires social contagion to work at the evaluation level
through belief updating and signaling of positive post-adoption outcomes (Hinz et al. 2011, p.
68; Iyengar, Van den Bulte, and Lee 2015, p. 18; Iyengar, Van den Bulte, and Valente 2011,
p. 210). Their superior influence stems not from their increased persuasiveness, but rather
from their heavy usage or high levels of participation and active referral (Godes and Mayzlin
2009, p. 737; Hinz et al. 2011, p. 68; Iyengar, Van den Bulte, and Valente 2011, p. 210).
3.2 Acceleration of Adoption
As mentioned earlier, the type of seeding strategy used in a viral marketing campaign
can affect the speed at which the adoption process occurs (Goldenberg et al. 2009, p. 10;
Libai, Muller, and Peres 2013, p. 161). In particular, early customer acquisition due to the
acceleration of the adoption process can directly result in higher economic outcomes because
of the time value of money (Libai, Muller, and Peres 2013, p. 173). Therefore, it is important
that companies understand the role of hubs in adoption acceleration, and the benefits it brings.
We will review some studies done on this effect in order to answer the questions: How does
hub seeding accelerate adoption? What economic benefits does it bring?
In order to conceivably be able to accelerate the diffusion process, hubs themselves
will have to be early adopters (Goldenberg et al. 2009, p. 3). As established earlier, while
innovator hubs may naturally adopt first due to their innovativeness, follower hubs will only
adopt early if they are sufficiently exposed to other early adopters (Goldenberg et al. 2009, p.
4). Using data from a large Korean social network website that included information about the
adoptions timing of 30,723 hubs and 289,001 non-hubs, Goldenberg et al. (2009, p. 10)
empirically show that hubs tend to adopt earlier than others, and the reason for that is their
large number of connections rather than their innovativeness, since triggering the adoption of
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hubs requires 1.68 early adopting neighbors, as compared to only 0.61 for non-hubs.
In contrast to the stimulation results of Watts and Dodds (2007, p. 454), they argue
that it is the exposure to an absolute number, and not the proportion, of already adopted
connections that drives people to also adopt (Goldenberg et al. 2009, p. 7). Therefore,
Goldenberg et al. (2009, p. 4) suggest that even if one takes the prudent assumption that hubs
are not more persuasive than others, their early adoption will nevertheless activate more
connections and significantly increase the overall rate of adoption.
This suggestion is supported by the results of several regression analyses performed in
their study, which demonstrate that hubs indeed accelerate the overall adoption process
(Goldenberg et al. 2009, p. 8). More precisely, they find that hubs with higher in-degree adopt
earlier, while hubs with higher out-degree are more effective in speeding the adoption of
others (Goldenberg et al. 2009, p. 8). It is also observed that the innovator hubs’ effect on the
speed of the adoption process is more than twice that of follower hubs, since they adopt
earlier and have more time to affect the social network (Goldenberg et al. 2009, p. 9).
These findings are in line with a study done by Katona, Zubcsek, and Sarvary (2011,
p. 431) using data from a major European social network website, which includes adoption
information of more than 4 million registered users for a period of 3.5 years. The study finds
empirical support for the so-called “degree effect”, whereby individuals who are connected to
many already adopted people have greater adoption probability because they are provided
with more information about the innovation in question (Katona, Zubcsek, and Sarvary 2011,
p. 426). Their statistical analysis also further confirms the positive correlation between an
individual’s number of already adopted connections and his or her adoption probability for
both hubs and non-hubs (Katona, Zubcsek, and Sarvary 2011, p. 441). Although they observe
that it is the proportion, not the absolute number, of already adopted connections that explains
more about adoption behavior, they still find evidence of hubs adopting earlier due to their
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larger number of exposures to the innovation (Katona, Zubcsek, and Sarvary 2011, p. 438).
The results of another empirical study done by Risselada, Verhoef, and Bijmolt (2014,
p. 52) also corroborate Goldenberg et al.’s (2009, p. 4) argument. Using data from a large and
random sample of 15,700 customers of a Dutch mobile telecommunications service provider,
they examined the consumer adoption behavior of a new smartphone and found a positive
correlation between the absolute number of cumulative adoptions within an individual’s
network and his or her adoption probability even after accounting for direct mass marketing
efforts by the company (Risselada, Verhoef, and Bijmolt 2014, p. 65).
Similar results were obtained by De Matos, Ferreira, and Krackhardt (2014, p. 1103)
in their study on the diffusion of iPhone 3G between August 2008 and June 2009 over a large
social network, which used the detailed call records of 24,131 users provided by a major
European mobile carrier in one country. They observe that people’s decision to adopt the
iPhone 3G is dependent on whether their connections have already adopted this smartphone
even after controlling for heterogeneity across regions, demographics, and time (De Matos,
Ferreira, and Krackhardt 2014, p. 1129). More precisely, they show that if all of a person’s
connections adopt the iPhone 3G, then his or her adoption probability increases on average by
15% (De Matos, Ferreira, and Krackhardt 2014, p. 1129).
Further, a study conducted by Libai, Muller, and Peres (2013, p. 162) used empirical
connectivity and adoption data from 12 different social networks, including YouTube, CNET,
and URV e-mail networks, as inputs to an agent-based stimulation model. For each of the 12
networks, they ran and compared diffusion stimulations of a new product with 4 different
seeding scenarios: (1) no seeding program, (2) random seeding program, (3) hubs seeding
program, and (4) experts seeding program (Libai, Muller, and Peres 2013, p. 168).
They find that the hubs seeding program is more effective in accelerating the adoption
process than other seeding programs with 31.5% of its customer equity gained attributable to
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16!
the acceleration as compared to 28.4% for experts seeding and only 25.9% for random
seeding (Libai, Muller, and Peres 2013, p. 168). They argue that because seeding several hubs
simultaneously might cause their sphere of influence to overlap, and this overlap would grow
as diffusion progresses, the contagion they create can become accelerated (Libai, Muller, and
Peres 2013, p. 173). This ties in with Goldenberg et al.’s (2009, p. 4) suggestion that hubs
accelerate the adoption process by activating more connections than others.
Libai, Muller, and Peres (2013, p. 172) also studied the value created by seeding
programs, considering both the number of people affected as well as the actual increased
profitability resulting from the time value of money. They find that the adoption horizon of
new products is long enough such that acceleration will indeed have a significant positive
impact on a company’s net profits (Libai, Muller, and Peres 2013, p. 172). This is consistent
with Iyengar, Van den Bulte, and Valente’s (2011, p. 211) findings about the higher customer
lifetime value of early adopting hubs as well as their higher “network value”, given that they
activate more people earlier, and therefore accelerating the overall adoption process.
The economic benefits resulting from adoption acceleration can also be affected by
three factors. First, if the price or markup of the new product are expected to decline with
time, presumably due to competitive pressure, the economic benefits of acceleration will be
greater because customers who adopt earlier will be more profitable than those who adopt
later (Libai, Muller, and Peres 2013, p. 173). Second, a lower customer retention rate reduces
the economic benefits of acceleration because the spread of disadoption on others begins
earlier and therefore, the company loses profits faster over time (Libai, Muller, and Peres
2013, p. 173). Third, because the brand strength of the new product indicates its monopolistic
position relative to its competition, stronger brands will proportionately profit more from the
acceleration rather than the expansion effects of a seeding program, since they require less
help to cope with market competition (Libai, Muller, and Peres 2013, p. 173).
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In addition, Ho et al. (2012, p. 236) also state that the value of a customer (CV) should
be measured by both her purchase value (PV) and her influence value (IV), and therefore if
the customer has influence on a large number of people, it is possible that her IV can be far
greater than her PV in the customer value equation (CV = PV + IV). Building on Van den
Bulte and Joshi’s (2007, p. 400) model asymmetric influence, they developed a model
framework to quantify PV, IV, and CV of customers according to their timing of adoption
(Ho et al. 2012, p. 236). They find that even when companies offer purchase discounts to
induce adoption acceleration, the resulting increase in IV often overcompensates the decrease
in PV and causes an overall increase in the total customer value (Ho et al. 2012, p. 251).
In summary, the presented literature demonstrates that seeding hubs can indeed
accelerate the adoption process because hubs tend to adopt earlier (Goldenberg et al. 2009, p.
4; Katona, Zubcsek, and Sarvary 2011, p. 438). These early adopting hubs then activate large
numbers of connections and in turn trigger the adoption of even more people (De Matos,
Ferreira, and Krackhardt 2014, p. 1129; Goldenberg et al. 2009, p. 4; Risselada, Verhoef, and
Bijmolt 2014, p. 65). Due to time value of money, adoption acceleration will also bring
economic benefits through its increase in customer lifetime values (Ho et al. 2012, p. 251;
Iyengar, Van den Bulte, and Valente 2011, p. 211; Libai, Muller, and Peres 2013, p. 172).
3.3 Expansion of Market Size
Viral marketing campaigns can also have the ability to expand the eventual market
size, since the presence of word-of-mouth can trigger adoption in people who may otherwise
not know enough about the new product (Libai, Muller, and Peres 2013, p. 163). While the
value generated by market expansion may be intuitive, its contribution in relation to adoption
acceleration can be ambiguous (Libai, Muller, and Peres 2013, p. 173). Hence, by reviewing
previous studies done, we seek to answer the questions: How does hub seeding expand the
market size? How does its economic benefits compare with that of accelerated adoption?
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Goldenberg et al. (2009, p. 4) argue in their aforementioned study that since people
are provided indirect information by their connections, those who have a large number of
connections will also inevitably possess larger amounts of information. From the network
perspective, access to a diverse source of information will require a person to have both high
levels of degree and betweenness (Goldenberg et al. 2009, p. 4). These centralities are usually
correlated partly because people who have large number of connections are also more likely
to be connected to different parts of the network (Goldenberg et al. 2009, p. 4).
Therefore, Goldenberg et al. (2009, p. 4) suggest that even with the conservative
assumption that hubs may not be more persuasive than others, their large number of
connections will allow them to reach into different parts of the network where people may not
otherwise come into contact with the new product. The adoption probability of these people
will increase, if a sufficiently large number of hubs adopts the new product (Goldenberg et al.
2009, p. 4). Conversely, if hubs are taken away from the network, these people may not be
exposed to the product enough to trigger their adoption (Goldenberg et al. 2009, p. 4).
Therefore, seeding hubs can increase the number of exposures for these people and expand
the eventual market size (Goldenberg et al. 2009, p. 4).
Using regression analysis, Goldenberg et al. (2009, p. 9) find empirical support for the
suggestion with results indicating a strong positive correlation between hub adoption and the
eventual size of the market. This finding is line with Libai, Muller, and Peres’ (2013, p. 169)
results, which show that the hubs seeding program is more effective in generating more
customer equity. The hubs seeding program achieved 104.5% additional customer equity as
compared to the no-seeding scenario, while experts seeding and random seeding achieved
only 90.6% and 80.2% respectively (Libai, Muller, and Peres 2013, p. 168).
More interestingly, Goldenberg et al. (2009, p. 9) observe that follower hubs’ impact
on the market size are about seven times more than that of innovator hubs (Goldenberg et al.
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2009, p. 9). They explain that since follower hubs are more similar to the people in the main
market in terms of innovativeness, their adoption tends to have more impact on them because
similar people find it much easier to trust each other (Goldenberg et al. 2009, p. 4). This
phenomenon is called homophily and it fosters trust between people with similar traits or
preferences (McPherson, Smith-Lovin, and Cook 2001, p. 415). Although homophily can
become an obstacle to diffusion across groups, its existence in a coherent market can enhance
the process (Goldenberg et al. 2009, p. 4). Hence, while innovator hubs also have more
impact on the early market due to the effect of homophily, follower hubs will still have a
larger impact on the overall market size because the main market is typically larger than the
early market (Goldenberg et al. 2009, p. 4).
The effect of homophily on adoption is confirmed by Risselada, Verhoef, and
Bijmolt’s (2014, p. 65) study on the adoption of a smartphone that is described in the previous
section. Their analysis reveals that homophily is an important social influence variable in
affecting adoption, especially through the absolute number of cumulative adoption (Risselada,
Verhoef, and Bijmolt 2014, p. 65). They explain that in the long run, the absolute number of
cumulative adoption by homophilous others can possibly signal to a person what the norm in
his or her network has become, and therefore set a higher normative pressure on him or her to
adopt (Risselada, Verhoef, and Bijmolt 2014, p. 65).
Likewise, De Matos, Ferreira, and Krackhardt’s (2014, p. 1114) aforementioned study
on the diffusion of iPhone 3G also demonstrates the existence of homophily effects in the
adoption process. By using community dummies as controls, they successfully separate the
unobservable homophily effects from the peer influence effects on network formation and
adoption timing (De Matos, Ferreira, and Krackhardt 2014, p. 1114). This shows that
homophily can exist and affect the diffusion process even if similar preferences or traits may
not be observable initially (De Matos, Ferreira, and Krackhardt 2014, p. 1105).
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The abovementioned study conducted by Libai, Muller, and Peres (2013, p. 161)
shows that the economic benefits generated by seeding programs is contributed by both the
effects of adoption acceleration due to the time value of money as well as market expansion
because of the marginal profits resulting from additional customer acquisition. They calculate
the economic benefits generated by the effect of market expansion under the assumption of a
two-competitor setting and the full adoption of the market by the end of the time horizon,
such that any customer lost by one competitor will be gained by the other (Libai, Muller, and
Peres 2013, p. 168). Their results show that market expansion contributes significantly more
than adoption acceleration (about 70% versus 30%) to the total economic value generated
across network types (Libai, Muller, and Peres 2013, p. 173).
However, this ratio can differ greatly depending on three market conditions (Libai,
Muller, and Peres 2013, p. 173). First, increasing discount rates or decreasing product price
markups, presumably due to competition, will decrease the proportion of value created by
market expansion because lower future value of customers means that accelerated adoption
can capture relatively more benefits over time (Libai, Muller, and Peres 2013, p. 170).
Second, higher customer retention rates or lower disadoption rates will decrease the
proportion of value generated by market expansion because in this case, the future customer
value is higher, and therefore accelerated adoption will be able to capture relatively more
value (Libai, Muller, and Peres 2013, p. 171). Third, weaker brands will benefit more from
market expansion than adoption acceleration because they generally need more help in
competing for market share in the long-term (Libai, Muller, and Peres 2013, p. 169).
It is important to distinguish between the benefits of a seeding program that are
derived from the market expansion effect and that from the acceleration expansion effect
because of the strategic implications (Libai, Muller, and Peres 2013, p. 162). When
companies make short-term viral marketing plans, they may overestimate their seeding
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program’s potential economic benefits due to the misinterpretation of adoption acceleration as
market expansion (Libai, Muller, and Peres 2013, p. 162).
In summary, the reviewed literature shows that seeding hubs can have a positive
impact on the eventual market size because the extensive reach of their large number of
connections provides critical exposures of the new product to people who may otherwise not
adopt (Goldenberg et al. 2009, p. 4). Follower hub’s larger impact on market expansion can
be explained by the existence of homophily effects in the adoption process (De Matos,
Ferreira, and Krackhardt 2014, p. 1114; Goldenberg et al. 2009, p. 4; Risselada, Verhoef, and
Bijmolt 2014, p. 65). In a seeding program, market expansion creates proportionally more
economic value than adoption acceleration (Libai, Muller, and Peres 2013, p. 173).
4. Discussion
4.1 Critical Evaluation
The presented literature demonstrates some of the positive effects related to hub
seeding strategy in viral marketing campaigns. However, one has to be very careful not to
assume that these findings can be applicable to all product categories or across all socio-
economic and competitive market conditions. For example, the aforementioned studies
conducted by Iyengar, Van den Bulte, and Valente (2011, p. 210) as well as Godes and
Mayzlin (2009, p. 721) have shown us that the operations of contagion can differ drastically
between complex products with high perceived risk and those with low perceived risk.
Likewise, Libai, Muller, and Peres (2013, p. 173) have also demonstrated how various
competitive market conditions can have an impact on the effects and economic outcomes of a
seeding program. Additionally, it would also be rather unrealistic to assume that certain
sociological factors such as the effects of homophily, as proposed by Goldenberg et al. (2009,
p. 4), and the presence of observational learning, as mentioned by Iyengar, Van den Bulte,
and Valente (2011, p. 211), should have the same effect across different ethnicities, age
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groups, and income levels. Hence, the role of hubs and their effects on viral marketing
campaigns cannot be generalized to be applicable in all situations. Instead, their practical
relevance depends on the specific product categories and market conditions involved.
Further, many of the presented studies fail to consider how non-complying hubs may
negatively affect the diffusion process and possibly offset the abovementioned effects of the
hub seeding strategy. For example, hubs may not always adopt the new product seeded to
them. They can resist adoption especially when the new product does not match their norms
and beliefs (Iyengar, Van den Bulte, and Valente 2011, p. 211). Even if we assume that these
hubs would not spread negative information about the new product, their very act of
resistance may serve as a signal for observational learning by other members in the network
and have a negative impact on the diffusion process, as the opposite was suggested by
Iyengar, Van den Bulte, and Valente (2011, p. 210). More importantly, this negative impact
can also potentially be amplified through other hubs when we consider the potential effect of
homophily among hubs, as suggested by Goldenberg et al. (2009, p. 4).
In addition, some studies that used observational data from online social networks fail
to take into consideration that many users also communicate over many other channels such
as face-to-face meetings, e-mail, telephone, and other social media platforms. For example,
De Matos, Ferreira, and Krackhardt (2014, p. 1130) noted in their study that although they
knew many users have interacted through many other channels, they were not able to measure
it. From this perspective, experimental data may have an advantage, given the possibility to
design a setting to either include of exclude communications over other channels.
Last but not least, many studies still fail to make a clear distinction between trial and
adoption behavior. As mentioned by Taylor (1977, p. 105), the act of trying out a new product
can be a precondition for long-term adoption. Similarly, when these two terms are rigorously
defined, the decision to discontinue a trial should not be treated the same as disadoption.
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4.2 Managerial Implications
These studies provide a better understanding as to how and why seeding hubs may
seem to produce relatively better outcomes that can be very attractive for viral marketing
practitioners. Firstly, because hubs are found to be more influential than others only when
certain product categories are involved, managers must assess the relevance to their case
before pursuing the hub seeding strategy. Since hubs primarily derive their superior influence
from increased activity (Hinz et al. 2011, p. 68; Iyengar, Van den Bulte, and Valente 2011, p.
210), managers may want to target heavy and active users in an online social network.
Secondly, managers who want to justify the hub seeding strategy economically will
find the prospect of hubs adopting early and accelerating the overall adoption process
particularly attractive because it implies higher customer lifetime values (Iyengar, Van den
Bulte, and Valente 2011, p. 211; Libai, Muller, and Peres 2013, p. 172). Strategically
speaking, the accelerated adoption can also help managers to better realize any first-mover
advantage, which may be considered essential in sectors such as consumer electronics.
Thirdly, the expansion effects of seeding hubs can be particularly useful to managers
whose goal is to maximize market share. Through the potential activation of hubs who also
measure high in the betweenness centrality (Goldenberg et al. 2009, p. 4), managers may be
able to also tap into previously unknown markets. As suggested by Libai, Muller, and Peres
(2013, p. 173), although the market expansion effect generally contributes more economic
value, managers should still consider the impact of their competitive strength such as
branding and bargaining power before deciding on which effect to focus on.
Lastly, managers must consider the costs of identifying and seeding hubs especially
when they want to implement incentives. For example, De Matos, Ferreira, and Krackhardt
(2014, p. 1128) propose that in seeding expensive products such as iPhones, it might more
cost efficient to offer them at a discount instead of giving them away for free.
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4.3 Future Research
The literature presented here is by no means comprehensive or exhaustive. Watts and
Peretti (2007, p. 22) encourage companies to take the more pragmatic view of using random
but large-scale seeding programs in order to avoid having to rely their viral marketing success
on the potentially unproductive task of identifying and seeding influentials.
However, in the ever more crowded social media platforms, where thousands if not
millions of marketing messages compete everyday for attention and contagion, the effects of
influentials can be crucial in the success of a viral marketing campaign. More specifically,
since some studies show that seeding bridges can be advantageous because of their extensive
network reach (e.g. Granovetter 1973, p. 1366; Hinz et al. 2011, p. 60), and that the measures
of degree and betweenness centrality may not necessarily be mutually exclusive but rather
correlated (e.g. Goldenberg et al. 2009, p. 4), it may be fruitful to research on the behavior
and impact of high degree hubs who also measure high in betweenness centrality.
Finally, as mentioned by Hinz et al. (2011, p. 69), researchers should not only focus
on individual decisions and assume that the reactions of other members in the social network
are exogenous. Instead, more research attention should also be given to the development of
robust marketing response models that can integrate the realistic dynamics and interactions
between all members of a social network (Hinz et al. 2011, p. 69).
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Appendix: Literature Review Tables
Author/s (Year) [Journal] Research Focus Theoretical
Background Samples Method/Analysis Main Findings
Bampo, Mauro, Michael T. Ewing, Dineli R. Mather,
David Stewart, and Mark Wallace (2008) [Information Systems
Research]
Effects of Digital Social Network
Structures on Viral Marketing
Performance
Word-of-Mouth Communication
Modeling (through customer-generated characteristics and
behaviors variables)
• 39,000 self-selected target audience
• Comparative Computer Simulations
• Sensitivity Analysis
• Social network structures are important to the performance of viral campaigns
• Scale-free networks are more efficient for viral marketing
• Little differences between small world and random networks
Bass, Frank M. (1969) [Management
Science]
Growth Modeling of New Products
Theory of Innovation Diffusion and
Adoption Not Applicable • Mathematical
Modeling
• Timing of adoption is related to the number of previous adopters
Berger, Jonah and Katherine L.
Milkman (2012) [Journal of Marketing
Research]
Effects of Content Characteristics on Viral Marketing
Performance
Social Transmission and Emotional
Valence
• 6956 New York Times Articles
• Experiments (n=47, n=49)
• Logistic Regression Analysis
• Controlled Experiments
• Positive content is more
viral than negative content • High-arousal content is
more viral than low-arousal content
Berger, Jonah and Eric M. Schwartz (2011) [Journal of
Marketing Research]
Psychological
Drivers of Immediate and
Ongoing Word-of-Mouth
Theory of Interpersonal
Communications and Word-of-Mouth Communication
• Face-to-face conservations of >300 items
• Field: 1687 BzzAgents
• Lab: 120 Ordinary people
• Poisson Log Normal Regression Analysis
• Field and Lab Experiments
• More interesting products get more immediate but not ongoing word-of-mouth
• More publicly visible products get more of both immediate and ongoing word-of-mouth
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30!
Author/s (Year) [Journal] Research Focus Theoretical
Background Samples Method/Analysis Main Findings
De Bruyn, Arnaud and Gary L. Lilien
(2008) [International Journal of Research
in Marketing]
Multi-stage Model of Word-of-Mouth in Viral Marketing
Theory of Multi-stage Decision-Making and
Word-of-Mouth Communication
• Senders: 4500 Students from a US university
• Recipients: 1100
• Internet-based (e-mails) field study
• Logit Model Analysis
• Tie strength facilitated awareness
• Perceptual affinity triggered interest
• Demographic similarity had a negative influence on all stages of the process
De Matos, Miguel Godinho, Pedro
Ferreira, and David Krackhardt (2014) [MIS Quarterly]
Effects of Peer Influence in the
Diffusion of High-tech Products
Theory of Innovation Diffusion and
Adoption
• 4,986,313 Subscribers of a Major European Mobile Carrier
• Observational Study
• Meta-Analysis using SIENA
• Agent-based Modeling
• Propensity of someone to adopt increases with the percentage of friends who already have adopted
• If all friends adopted, the adoption probability increase by 15% on average
Feick, Lawrence F. and Linda L. Price (1987) [Journal of
Marketing]
Diffusers of Marketplace Information
(The concept of Market Mavens)
Theory of Interpersonal Influence and
Communication
• 1531 US Households
• Telephone questionnaires
• Factor Analysis (using LISREL)
• Consumers believe market mavens are influential
• Market maven is distinct from other influencers such as opinion leaders
Galeotti, Andrea and Sanjeev Goyal (2009)
[RAND Journal of Economics]
Impact of Influencers on
Strategic Diffusion
Theory of Innovation Diffusion and
Adoption Not Applicable • Mathematical
Modeling
• Optimal use of social networks leads to higher sales and greater profits
• Optimal to seed the least connected people if adoption probability increases with absolute number of adopters
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!
31!
Author/s (Year) [Journal] Research Focus Theoretical
Background Samples Method/Analysis Main Findings
Godes, David and Dina Mayzlin (2009) [Marketing Science]
Effectiveness of Firm-Created
Word-of-Mouth Communication
Theory of Diffusion and Word-of-Mouth
Communications
• 381 Customers and 692 Non-customers of a Restaurant Chain
• Field Test • Follow-up online
experiment • Various
Regression Analyses
• Light users are more effective than heavy users at driving the spread of information only at the initial awareness level
• Opinion leaders are not always more effective in spreading word-of-mouth
Godes, David et al. (2005) [Marketing
Letters]
Role of Firms in Consumer Social
Interactions
Theory of Diffusion and Influence of
Social Interactions Not Applicable • Literature
Review
• 4 Roles of firms in WOM: Observer, Moderator, Mediator and Participant
Goldenberg, Jacob, Sangman Han,
Donald R. Lehmann, and Jae Weon Hong (2009) [Journal of
Marketing]
Role of Hubs in the Diffusion and
Adoption Process
Theory of Innovation Diffusion and Interpersonal
Influence
• 30,723 hubs and 289,001 non-hubs from Cyworld – a Korean online social network
• Logistic Regression Analysis
• Agent-based Modeling
• Mainly 2 types of hubs:
Innovator and Follower hubs • Hubs adopt earlier because
of their larger number of connections
• Innovator hubs mainly accelerate adoption process
• Follower hubs mainly influence the market size
• Hub adoption can predict eventual product success
Granovetter, Mark S. (1973) [American
Journal of Sociology]
Impact of Weak Ties on Social
Networks
Theory of Diffusion, Mobility Opportunity,
and Community Organization
Not Applicable • Qualitative
Network Analysis
• Weak ties are indispensable for individual opportunities
• Weak ties act as local bridges and can reach more people in diffusion
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Author/s (Year) [Journal] Research Focus Theoretical
Background Samples Method/Analysis Main Findings
Hinz, Oliver, Bernd Skiera, Christian
Barrot, and Jan U. Becker (2011)
[Journal of Marketing]
Impact of Seeding Strategies on the Performance of Viral Marketing
Campaigns
Theory of Diffusion, Word-of-Mouth
Communications, and Social Contagion
• Study 1: 120 nodes (controlled experiment)
• Study 2: 1,380 nodes (field experiment)
• Study 3: 208,829 nodes (real-world data)
• 2 Experimental Studies
• 1 Ex-post Real-world Study
• Various Regression Analyses
• Seeding hubs and bridges are up to 8 times more effective than random and fringe seeding
• Hubs are superior is due to increased activity
• Hubs are not necessarily more persuasive than others
• Hubs are more likely to engage because viral marketing works mostly through information transfer and belief updating
Ho, Teck-Hua, Shan Li, So-Eun Park, and Zuo-Jun Max Shen (2012) [Marketing
Science]
Value of Customer Influence and
Adoption Acceleration
Two-Segment Asymmetric Influence Model in Innovation
Diffusion and Adoption
• Adoption Data of 19 Music CDs
• Analysis of Data from Van den Bulte and Joshi (2007)
• Mathematical Modeling
• Customer values of early adopters are higher due to their higher influence values
• Adoption acceleration leads to significant increase in total customer value
Iyengar, Raghuram, Christophe Van den
Bulte, and Jae Young Lee (2015)
[Marketing Science]
Effects of Peer Influence in New Product Trial and
Repeat
Theory of Innovation Diffusion, Adoption, and Word-of-Mouth
Communications
Not Applicable
• Analysis of Data from Iyengar, Van den Bulte, and Valente (2011)
• Mathematical Modeling
• Social contagion exists in both trial and repeat
• Hubs are more influential in trial but not repeat
• Information transfer reduce risk in trial and normative pressure conformity in repeat
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Author/s (Year) [Journal] Research Focus Theoretical
Background Samples Method/Analysis Main Findings
Iyengar, Raghuram, Christophe Van den Bulte, Thomas W.
Valente (2011) [Marketing Science]
Effects of Opinion Leadership in
Social Contagion and Adoption
Theory of Diffusion, Interpersonal Influence, and
Word-of-Mouth Communications
• 67 Doctors from San Francisco
• 57 Doctors from LA
• 69 Doctors in New York City
• Survey of Physicians and Prescriptions
• Discrete-time Hazard Modeling
• Social contagion exists even after controlling for marketing effort
• Adoption affected by peers’ usage volume rather than their mere adoption
• Hubs tend to adopt earlier than self-reported leaders
Katona, Zsolt, Peter Pal Zubcsek, and Miklos Sarvary
(2011) [Journal of Marketing Research]
Effects of Personal Influences in Diffusion and
Adoption
Theory of Diffusion, Interpersonal Influence, and
Word-of-Mouth Communications
• 250,000 users of a European social network website
• Hazard-rate Modeling
• Log-Log Regression Analysis
• People who are connected to many adopters are more likely to adopt
• Influential power per contact decreases with the total number of contacts
Kiss, Christine and Martin Bichler (2008)
[Decision Support Systems]
Impact of Seeding Various Centrality Measures in Viral
Marketing
Network and Diffusion Theory
• Customer Network Data of a Telco
• Computational Experiments
• Computer Stimulations
• Central customers (notably out-degree among various centrality measures) performs very well in message diffusion
Leskovec, Jure, Lada A. Adamic, and
Bernardo A. Huberman (2007)
[ACM Transactions on the Web]
Effectiveness of Recommendations in Diffusion and
Adoption
Theory of Diffusion, Adoption, and
Word-of-Mouth Communications
• 15,646,121 Referrals
• 3,943084 Distinct Users
• 548,523 Different Products
• Statistical Analysis of a Large Retailer’s Referral Program
• Stochastic Modeling
• Average recommendations are not very effective at inducing adoption and do not spread very far
• Hubs’ success per recommendation declines with the number of recommendations sent
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Author/s (Year) [Journal] Research Focus Theoretical
Background Samples Method/Analysis Main Findings
Libai, Barak et al. (2010) [Journal of Service Research]
Effects and Consequences of C2C Interactions
Theory of Interpersonal and Word-of-Mouth Communications
Not Applicable • Literature Review
• Recent C2C theories and research findings
• Outline of interesting areas for future research
Libai, Barak, Eitan Muller, and Renana
Peres (2013) [Journal of Marketing
Research]
Value of the Acceleration and
Expansion Effects of Word-of-Mouth Seeding Programs
Theory of Diffusion, Adoption, and
Word-of-Mouth Communications
• 43,337 Nodes from 12 various social networks
• Average: 3611 Nodes/network
• Agent-based Modeling
• Various Regression Analysis
• Generally, expansion contributes more value than acceleration (70% vs. 30%)
• This ratio depends on expected future pricing, brand strength, and customer retention rates
• Hubs and experts can better accelerate adoption
McPherson, Miller, Lynn Smith-Lovin, and James M Cook
(2001) [Annual Review of Sociology]
Effects of Homophily in
Social Networks Network Theory Not Applicable • Literature
Review
• People trust information from other people who have similar traits as them
• Ties between non-similar people dissolve faster
Peres, Renana, Eitan Muller, and Vijay Mahajan (2010) [International
Journal of Research in Marketing]
New Product Diffusion and
Growth Models
Theory of Innovation Diffusion and
Adoption Not Applicable • Literature
Review
• Review of current research in diffusion within markets and technologies as well as across markets and brands
• Outline of the directions for future research
Porter, Elise Constance and Naveen Donthu
(2008) [Management Science]
Impact of firms in Cultivating Trust and Harvesting
Values in Virtual Communities
Attribution Theory and Word-of-Mouth
Communications
• Pretest 1 / 2: n=103 / n=42
• Main Study: 663 Customers
• Online Survey • Structural
Equation Modeling
• Positive effects of quality content and efforts to foster member embeddedness
• Information overload of highly connected people
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Author/s (Year) [Journal] Research Focus Theoretical
Background Samples Method/Analysis Main Findings
Richins, Marsha L. and Teri Root-Shaffer (1988) [Advances in Consumer Research]
Effects of Involvement and
Opinion Leaders in Word-of-Mouth
Interpersonal Influence Theory and
Word-of-Mouth Communications
• 217 Adult Consumers
• 53 New Car Owners
• Questionnaires via Mailing
• Path Analysis
• Enduring involvement is related to opinion leadership
• Opinion leaders are effective spreaders of word-of-mouth
Risselada, Hans, Peter C. Verhoef, and
Tammo H.A. Bijmolt (2014) [Journal of
Marketing]
Effects of Social Influence on the
Adoption of High-tech Products
Theory of Diffusion, Adoption and
Network Analysis
• 15,700 Random Customers from a Dutch Mobile Operator
• Fractional Polynomial Hazard Modeling
• Computer Stimulations
• Social influence affects adoptions even after controlling for direct marketing effects
• Tie strength and homophily are important factors of social influence
• Each additional adopter has a positive impact on the adoption of others and this impact does not decrease
• Influence of recent adoption remain equal over time
Taylor, James W. (1977) [Journal of
Marketing Research]
Characteristics of Innovators and
Early Triers
Attribution and Diffusion Theory
• 11 New Consumer Goods
• ANOVA • MRT
Comparisons
• Innovativeness is dependent on product class use
Van den Bulte, Christophe and Yogesh V. Joshi
(2007) [Marketing Science]
Two-Segment Asymmetric
Influence Model in Innovation Diffusion
Theory of Diffusion, Social Character, and
Two-Step Flow Model
33 Adoption Data Sets Including: • New Antibiotic
among 125 Physicans
• 19 Music CDs • 5 High-tech
Equipments • CT Scanners
• Statistical Analyses
• Mathematical Modeling
• Diffusion curve with a influentials and imitators can exhibit a dip between early and later parts
• Two-segment model fits better than the standard mixed-influence models
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Author/s (Year) [Journal] Research Focus Theoretical
Background Samples Method/Analysis Main Findings
Van den Bulte, Christophe and Gary
L. Lilien (2001) [American Journal of
Sociology]
Social Contagion or Marketing Effort
Drives Adoption
Theory of Diffusion, Adoption, and Social
Influences
• 125 General Practitioners (Data from Coleman et al. 1966)
• Statistical Analyses
• Hazard-rate Modeling
• Marketing effort drives adoption behavior
• Previous studies confound social contagion with effects of marketing efforts
Van der Lans, Ralf, Gerrit van Bruggen, Jehoshua Eliashberg, and Berend Wierenga
(2010) [Marketing Science]
Modeling the Performance of
Online Viral Marketing Campaigns
Theory of Diffusion and Word-of-Mouth
Communications
• 228,351 Participants of a Viral Campaign
• Mathematical Modeling
• Statistical Analyses
• A viral branching model that predicts the spread of word-of-mouth in online viral marketing campaigns
Venkatraman, Meera P. (1990) [Advances
in Consumer Research]
Relationship of Opinion Leadership
and Enduring Involvement
Theory of Interpersonal Influence and Involvement
• 317 University Students
• Baron and Kenny Framework
• Statistical Analyses
• Opinion leadership mediates the relationship between enduring involvement and information transfer
Watts, Duncan J. and Peter Sheridan Dodds
(2007) [Journal of Consumer Research]
Role of Influentials in Diffusion and Viral Marketing
Theory of Diffusion, Interpersonal
Influence, and Two-Step Flow Model
Not Applicable
• Computer Stimulations
• Mathematical Modeling
• Under most conditions, large cascades of influence is driven by a critical mass of easily influenced people, not by influentials
Watts, Duncan J. and Jonah Peretti (2007) [Harvard Business
Review]
Practical Seeding Strategy for Viral
Marketing
Theory of Diffusion and Word-of-Mouth
Communications Not Applicable • Conceptualization
• Big-seed marketing • Seeding sufficiently large
amount of people is more pragmatic than identifying and seeding the influentials
Weimann, Gabriel (1991) [Public
Opinion Quarterly]
Identifying the “influentials”
Theory of Interpersonal
Influence
• 650 Israelis • 270 Israeli
Kibbutz
• Questionnaires • Strength of
Personality Scale
• The 3 attributes of influence (1) who one is, (2) what one knows, (3) whom one knows
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Affidavit
I hereby declare that I have developed and written the enclosed bachelors thesis entirely on my own and have not used outside sources without declaration in the text. Any concepts or quotations attributable to outside sources are clearly cited as such. This bachelors thesis has not been submitted in the same or substantially similar version, not even in part, to any other authority for grading and has not been published elsewhere. I am aware of the fact that a misstatement may have serious legal consequences. Mannheim, 15 June 2015 Weiquan Alvin Liu