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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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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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


• 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


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


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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Godes, David and Dina Mayzlin (2009) [Marketing Science]

Effectiveness of Firm-Created


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


• 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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32!



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)


• 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

!

!

33!



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


• 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


• 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

!

!

34!



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


• 43,337 Nodes from 12 various social networks

• Average: 3611 Nodes/network


• 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


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

!

!

35!



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


• 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


• 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


• 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

!

!

36!



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)


• 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


Communications

• 228,351 Participants of a Viral Campaign



• 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


• 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



• 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


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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37!

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

Documents

Bachelor Thesis - The Role of Seeding Strategy