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Original Article
Introduction: The Disruption of
Traditional Advertising
For decades, the advertising industry was based on an asym-
metrical communication model, where marketers would
engage audiences via paid media channels. The advent of
social media platforms completely transformed the general
media landscape, along with the advertising model, as audi -

ences shifted from the role of content receivers to content
creators, distributors, and commentators (Keller, 2009; Scott,
2015). Simply put, the empowerment of audiences from
mere viewers to active content distributors effectively flipped
the advertising model on its head. Where paid media (in this
case, advertising) was once supported by earned and owned
media, the modern advertising model uses owned, shared,
and earned media as the key media planning strategy, sup-
ported by paid media (Pearson, 2016). Recognizing the
increased potential for free content distribution, marketers
realized that creating highly engaging advertising content
could expand potential reach, a cheaper and more credible
tactic than traditional paid advertising (Cho, Huh, & Faber,
2014; Golan & Zaidner, 2008). This fundamental disruption
of the advertising and marketing world led to growing
interest in content creation, co-creation, and distribution.
Generally defined, advertising refers to the “paid non-
personal communication from an identified sponsor using
mass media to persuade or influence an audience” (Wells,
Moriarty, & Burnett, 2000, p. 6). Consistent with most, but
not all, of these requirements, Porter and Golan (2006)
defined viral advertising as “unpaid peer-to-peer communi-
cation of provocative content originating from an identified
sponsor using the Internet to persuade or influence an audi-
ence to pass along the content to others” (p. 33).
The expanding literature on viral advertising recognizes
the ways in which peer-to-peer distribution of advertising
content are redefining the industry. When examined holisti -
cally, the literature has several limitations. First, existing
viral advertising research is limited primarily to advertising
spread within one step of the original source (e.g., predicting
the number of message shares), while information on social
847516 SMSXXX10.1177/2056305119847516Social Media

<span class="symbol" cstyle="Mathematical">+</span>
SocietyHimelboim and Golan
research-article20192019
1University of Georgia, USA
2University of South Florida, USA
Corresponding Author:
Itai Himelboim, Department of Advertising and Public
Relations, Grady
College of Journalism and Mass Communication, University of
Georgia,
Athens, GA 30602-3018, USA.
Email: [email protected]
A Social Networks Approach to Viral
Advertising: The Role of Primary,
Contextual, and Low Influencers
Itai Himelboim1 and Guy J. Golan2
Abstract
The diffusion of social networking platforms ushered in a new
age of peer-to-peer distributed online advertising content,
widely referred to as viral advertising. The current study
proposes a social networks approach to the study of viral
advertising
and identifying influencers. Expanding beyond the conventional
retweets metrics to include Twitter mentions as connection
in the network, this study identifies three groups of influencers,
based on their connectivity in their networks: Hubs, or
highly retweeted users, are Primary Influencers; Bridges, or
highly mentioned users who associate connect users who would
otherwise be disconnected, are Contextual Influencers, and
Isolates are the Low Influence users. Each of these users’ roles
in viral advertising is discussed and illustrated through the
Heineken’s Worlds Apart campaign as a case study. Providing a

unique examination of viral advertising from a network
paradigm, our study advances scholarship on social media
influencers
and their contribution to content virality on digital platforms.
Keywords
viral advertising, social networks, Twitter, viral marketing,
social media influencers
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2 Social Media + Society
media often spreads beyond a single step from the original
source. Second, in focusing on the characteristics of shared
content or sharing users, researchers make the assumption
that all shares are equal in terms of their impact. However,
sharing-impact varies among users, based on their connectiv-
ity. Third, the metaphor of virality, the idea that content is
spread gradually among individuals and their immediate
contacts, may not fully capture what is often a complex
multi-actor process of content distribution. Cascades of con-
tent distribution were found to be centered on a small num-
ber of distributors, creating a hierarchical, rather than
egalitarian, pattern of content distribution (Baños, Borge-
Holthoefer, & Moreno, 2013).
This study proposes a social networks approach to address
these limitations, using Heineken’s Worlds Apart campaign
as a case study. Data are collected for all Twitter users post-
ing links to the original advertisement on YouTube, and the

subsequent retweets and mention relationships. While a
growing body of scholarship examines the potential impact
of social media influencers in online marketing campaigns,
they often treat all influencers as one and the same (Evans,
Phua, Lim, & Jun, 2017; Phua & Kim, 2018).
We argue that different types of influencers impact social
networks in different degrees and ways. Informed by a body
of scholarship in social networks, we propose that there are
three types of influencers: primary, contextual, and low
influencers. Primary influencers are hubs, users who attract
large and disproportionate retweets from other users in the
network. Contextual influencers play a role of bridges in the
network by providing context regarding the overall discus-
sion and thus help to understand the distribution of content
beyond the quantity of retweets. Low influencers are users
who shared a link to online content; however, these users
were neither retweeted nor mentioned by anyone else in the
network. While low influencers have limited individual con-
tributions to content distribution, their aggregate influence is
substantial.
Social Media Influencers
An emergent body of scholarship in the field of marketing,
advertising, and public relations examines the intermediary
function of influencers between brands and consumers, orga-
nizations, and stakeholders in social media engagement (De
Veirman, Cauberghe, & Hudders, 2017; Freberg, Graham,
McGaughey, & Freberg, 2011; Phua, Jin, & Kim, 2016). At
the most basic level, influencer is identified by their number
of followers and their ability to impact social media conver-
sation regarding brands or topics (Watts & Dodds, 2007).
While the term social media influencer is ubiquitously
applied, there are few formal definitions of what an influ-
encer actually is. Brown and Hayes (2008) defined influenc-

ers broadly as individuals who hold influence over potential
buyers of a brand or product to aid in the marketing activities
of the brand. Others narrow the definition of an influencer to
reflect on the latest marketing trend in which social media
celebrities are paid by advertisers to promote products
(Abidin, 2016; Evans et al., 2017; Senft, 2008).
Moving beyond definitions, scholars attempt to theorize
why it is that some social media users grow more influential
than others via relationship building. To explain the influ-
ence of influencers, media scholars often depend on the
parasocial relationship explanation (Daniel, Crawford, &
Westerman, 2018; Lou & Yuan, 2018; Rasmussen, 2018).
Moving beyond a temporary parasocial interaction (as origi -
nally conceptualized by Horton & Wohl, 1956), parasocial
relationships between audience members and mediated
characters are formed over a period of time and provide
audience members with a sense of engagement with on-
screen characters (Klimmt, Hartmann, & Schramm, 2006;
Tukachinsky, 2010). In the context of social media, such
parasocial relationships provide influencers with unique
social capital that leads to audience trust (Tsai & Men, 2017;
Tsiotsou, 2015).
Indeed, the central role of trust in parasocial relation-
ships may provide a plausible explanation for the influencer
phenomenon and the rise of influencer marketing (Audrezet,
De Kerviler, & Moulard, 2018). Trust has been identified as
a key predictor of several advertising consequences includ-
ing recall, attitude, and likelihood to share (Cho et al.,
2014; Lou & Yuan, 2018; Okazaki, Katsukura, & Nishiyama,
2007). Abidin (2016), building on the concept of parasocial
relations, identified four ways that influencers appropriated
and mobilized intimacies: commercial, interactive, recipro-
cal, and disclosive. Influencers are identified not only based

on their sheer number of such parasocial relationships, such
as subscribers or followers on social media, but primarily
based on their ability to impact social media conversation
and subsequent behavior regarding brands or topics (Watts
& Dodds, 2007).
We propose to complement existing conceptualization of
influencers by shifting the focus from influencers’ engage-
ment or the nature of individual connections with them, to
their ability to reach large, unique, and relevant audiences
and to shape the conversation about brands and topics. It is
the distribution of content that allows influencers to influ-
ence, and therefore provides a key theoretical framework for
identifying social media influencers. We next discuss viral
advertising as a theoretical framework for content reach, fol -
lowed by its limitations. We then take a social networks
approach to theorize social media influencers, bridging both
bodies of literature.
Viral Advertising
As explained by Golan and Zaidner (2008), there are several
key differences between viral and traditional advertising.
First, viral advertising earns audience eyeballs, as opposed to
paying for them. This is a major departure from the tradi-
tional advertising exchange, where brands purchase media
Himelboim and Golan 3
space and interrupt an audience’s media consumption with
advertisements. Second, viral advertisements provide such
increased value to audiences that they transform audiences
from passive content receivers to active social distributors
who play a key role in advertisement distribution. Third,

although there are limited studies speaking to this point, it is
worth noting that information sharing has been shown to
increase a user’s followers on Twitter, which is a long-term
benefit for marketers (Hemsley, 2016).
What Makes Advertising Go Viral?
Why do some advertisements receive wide-scale viewership
via audience distribution, while others do not? Scholars offer
different approaches to this question, one focusing on con-
tent characteristics (Brown, Bhadury, & Pope, 2010; Golan
& Zaidner, 2008; Petrescu, 2014) and another examining
virality attribute factors such as brand relationships (Hayes
& King, 2014; Ketelaar et al., 2016; Shan & King, 2015).
Porter and Golan (2006) specifically identify provocative
content as contributing to advertising virality. Other studies
identify appeals to sexuality, as well as shock, violence, and
other inflammatory content as key elements of message viral -
ity (Brown et al., 2010; Golan & Zaidner, 2008; Petrescu,
2014). Eckler and Bolls (2011) argue that the emotional tone
of advertisement is directly related to audience intention to
forward ads to others. Yet advertising content, tone, and emo-
tion cannot fully account for ad virality. Scholars point to a
variety of other variables significantly related to advertising
virality including brand relationship (Hayes & King, 2014;
Ketelaar et al., 2016; Shan & King, 2015), attitude toward the
ad (Hsieh, Hsieh, & Tang, 2012; Huang, Su, Zhou, & Liu,
2013), and credibility of the sender/referrer (Cho et al., 2014;
Phelps, Lewis, Mobilio, Perry, & Raman, 2004).
Hayes, King, and Ramirez (2016) advanced research on
viral advertising by illustrating the importance of interper -
sonal relationship strength in referral acceptance. Their study
suggested that individuals are motivated to share advertising
content based on reputational enhanceme nt and reciprocal

altruism. Alhabash and McAlister (2015) conceptualized
virality based on three key components: viral reach, affective
evaluation, and message deliberation. The authors linked
virality and online audience behaviors in what they refer to
as viral behavioral intentions (VBI). This linkage is sup-
ported by later research indicating that the virality of digital
advertising is often related to several VBIs motivated by a
variety of audience-based characteristics (Alhabash, Baek,
Cunningham, & Hagerstrom, 2015; Alhabash et al., 2013).
Limitations of Viral Advertising Research
In essence, viral advertising represents a “peer-to-peer com-
munication” strategy that depends on distribution of content
(Petrescu & Korgaonkar, 2011; Porter & Golan, 2006).
Despite the fact that most peer-to-peer social media shares
include multiple distribution phases (e.g., from user A to user
B to user C), existing viral advertising research is mostly
limited to one-step advertisement spread (e.g., predicting
number of message shares). Studies suggest that while con-
tent may be shared by many users, most viral content is
spread beyond this single step (Bakshy, Hofman, Mason, &
Watts, 2011). The body of literature concerning viral adver -
tising does not examine advertising spread beyond a user’s
immediate set of connections.
Second, the literature conceptualizes virality based on
such sharing metrics as shares or retweets. In doing so, schol -
ars fail to account for the possibility that the overall i mpact
of such user actions may not result in equal content distribu-
tion outcomes. In fact, studies on virality of content and cas -
cades of information flow highlight that “popularity is largely
driven by the size of the largest broadcast” (Goel, Ander son,
Hofman, & Watts, 2015, p. 180). In other words, it is not only
the number of consumer-to-consumer interactions but the

connectivity of these consumers with others that determines
the impact of viral advertising. One user’s retweet may count
more than another user.
A third limitation is the more subtle assumption of virality
as metaphor. The idea that content is spread gradually from
one source to that source’s immediate small group of connec-
tions, to their neighbors, and so on is a powerful metaphor
that resonates well with many scholars (Miles, 2014; Porter
& Golan, 2006). However, research shows no foundation for
such an egalitarian assumption. Connections are distributed
in a skewed manner across individuals, a phenomenon
referred to in ways that vary by discipline:
in economics it goes by the name “fat tails,” in physics it is
referred to as “critical fluctuations,” in computer science and
biology it is “the edge of chaos,” and in demographics and
linguistics it is called “Zipf’s law.” (Newman, 2000)
At the end of the day, most pieces of shared content are not
re-shared by others, and thus are spread by very few.
Similarly, from an advertisement and social media perspec-
tive, Nielsen (2006) presented the “1-9-90 rule,” suggesting
that content is created by 1% of users and distributed by 9%
to the remaining 90% of content receivers. Baños et al.
(2013) showed that only a small minority of content dis-
tributors will account for content virality. In addition, Pei,
Muchnik, Andrade, Zheng, and Makse (2014) suggested
that “due to the lack of data and severe privacy restrictions
that limit access to behavioral data required to directly infer
performance of each user, it is important to develop and
validate social network topological measures capable to
identify superspreaders” (p. 8).
To address these key gaps in the literature of viral adver -
tising and subsequently our ability to theorize influential

users in terms of their content diffusion, we take a social net-
works approach, which focuses on patterns of connectivity
among users. We propose that social media influencers are
ultimately determined by their position in an issue or brand-
specific conversation network, allowing their posted content
to be distributed in a strategic manner. As such, these influ-
encers play key roles in the virality of any advertising cam-
paign on social media. A social networks approach, as
illustrated by Himelboim, Golan, Moon, and Suto (2014)
provides for a macro-understanding of social media relation-
ships, content flow, and the role of social media influencers
within the network.
The Social Networks Approach
The social networks conceptual framework shifts the focus
from individual traits to patterns of social relationships
(Wasserman & Faust, 1994). Applying a social networks
approach to social media activity allows researchers to cap-
ture content virality and identify key social media influenc-
ers that affect the conversation about a brand and reach key
groups of consumers. A social network is formed when con-
nections (“links”) are created among social actors (“nodes”),
such as individuals and organizations. The collections of
these connections aggregate into emergent patterns or net-
work structures. On Twitter, social networks are composed
of users and the connections they form with other users when
they retweet, mention, and reply to (Hansen, Shneiderman,
& Smith, 2011).
The network approach can bridge the viral advertising

and social media influencer’s bodies of literature. As dis-
cussed earlier, social media platforms allow individuals to
maintain parasocial relationships with influencers (Abidin,
2016). In the case of Twitter, such engagement is manifested
in the form of mentions, likes, and retweets. In social net-
works research, these relationships are conceptualized as
links in a network.
The social networks approach allows us to capture the
distribution of a specific piece of content (i.e., an advertise -
ment) and identify users in key positions in the network that
are responsible for the distribution of ads, as social media
influencers. It should be noted that even in studies on infor -
mation diffusion in related disciplines, it is quite rare to track
the virality of a single piece of content, rather than the over-
all diffusion of messages in a broader conversation.
Viral advertising research often focuses on the most visi -
ble type of content that is spread, shared, or retweeted on
Twitter. Social media influencers are often examined by their
number of connections in a social media platform (De
Veirman et al., 2017). However, a link to a video advertise-
ment, or any other source of paid advertising content, may be
posted by more than a single user who contributes to its dif-
fusion. In other words, while the advertisement itself may
have a single point of origin (e.g., a YouTube video page),
this advertisement may have multiple users who may account
for multiple points of origin for distribution on Twitter. While
a particular video may have gained many views and shares
on its platform of origin (“gone viral”), not all shares on
Twitter contributed equally to its virality. We therefore ini -
tialize our understanding of content distribution patterns by
asking,
RQ1: What is the distribution structure of a viral adver-

tisement on Twitter?
A single network can have different types of links, or ties,
that connect its users. On Twitter, users can be connected,
among others, by relationships of retweets and mentions. A
network of advertising virality captures users who posted
content with a hyperlink to a given ad. Such Twitter users
share a link to a given advertisement via a tweet, expanding
its reach one step away from the source (YouTube). Some
studies have examined the overall network structure to
explain virality. Pei et al. (2014) used social network analysis
on LiveJournal, Twitter, Facebook, and APS journals and
found that users who spread the most content were located in
the K-Core (a metrics of subgroup cohesiveness in the net-
work). At the node-level, a few users are expected to contrib-
ute further to the virality by having their tweets shared, or
retweeted, by many additional users. Such users capture
virality beyond a single step away from the source. Users
with many connections in the network are known as social
hubs (Goldenberg, Libai, & Muller, 2001) or simply Hubs.
Using computer simulations, Hinz, Skiera, Barrot, and
Becker (2011) found that seeding messages to hubs outper-
formed a random seeding strategy and seeding to low-degree
users, in terms of number of referrals. Kaplan and Haenlein
(2011) also illustrated the role that hubs play in integrative
social media and viral marketing campaigns.
Recognizing that the emergent literature on social media
influencers is somewhat undermined by the various uses of
the term influence to reflect different functions of influence,
we recommend the categorization of influencers into three
different types, based on the type of relationships, links in the
network, that makes them central in a network.
Social networks literature repeatedly shows that given the
opportunity to interact freely, connections among users will

be distributed unequally, as a few will enjoy large and dis-
proportionate number of relationships initiated with them,
while most will have very few ties. On Twitter, content
posted by a few users will enjoy major distribution via
retweeting, while the rest will gain little shares, if any.
Indeed, Araujo, Neijens, and Vliegenthart (2017), define
influentials as “users with above average ability to stimulate
retweets to their own messages” (p. 503), consistent with
conceptualization of influencers based on impact on content
distribution (Cha, Haddadi, Benevenuto, & Gummadi, 2010;
Kwak, Lee, Park, & Moon, 2010). Hubs as conceptualized in
social networks literature, therefore, are one type of social
media influencers as conceptualized in social media scholar -
ship, as each one makes a major contribution to content dis-
tribution. One type of influencer, from a social networks
conceptualization, is therefore the Primary Influencer, as it
is one of few members responsible for the distribution of
content in the network. We therefore present the following
research question:
RQ2: Which users serve as Primary Influencers in a viral
advertising network?
On Twitter, retweets are attributed to the original tweet;
therefore, operationalizing links in this network only as
retweets fails to capture information flow beyond one step
away from a user who shared a link to an ad. In other words,
since users are unlikely to share the same link more than
once, the network of retweets will create distinct subsets of
users, each retweeting a single tweet. These subsets are com-
pletely, or almost completely, disconnected from one another.

As discussed earlier, a key limitation of viral advertising lit-
erature is that studies are limited to the extent they measure
diffusion from a single source. In order to maximize insights
from the social networks approach to viral advertising, other
types of ties should be considered.
The practice of mentioning users on Twitter, using the @
symbol, serves two main purposes. First, it associates a post
with another user (e.g., an individual, an organization, a
brand), serving as metadata for that tweet. Second, it serves
as a secondary route of content distribution. When a tweet
mentions a given user, that tweet will appear on the recipi -
ent’s Notifications tabs and Home timeline view if the author
of the tweet follows the sender. Conceptualizing mentions on
Twitter as links in a social network captures the context of
the virality of advertisements by connecting users beyond
immediate retweeting of a single source. In other words, this
practice bridges the otherwise disconnected subsets of
retweeting users. In social network literature, bridging is a
concept that can advance the understanding of advertisement
virality and the key users who play a key role in it.
Bridges and Structural Holes
Burt’s (1992, 2001) theory of structural holes examines
social actors (e.g., individuals and organizations) in unique
positions in a social network, where they connect other actors
that otherwise would be less connected, if connected at all. In
Burt’s (2005) words, “A bridge is a (strong or weak) relation-
ship for which there is no effective indirect connection
through third parties. In other words, a bridge is a relation-
ship that spans a structural hole” (p. 24). A lack of relation-
ships among social actors, or groups of actors, in a network
gives those positioned in structural holes strategic benefits,
such as control, access to novel information, and resource
brokerage (Burt, 1992, 2001). Actors that fill structural holes

are viewed as attractive relationship partners precisely
because of their structural position and related advantages
(Burt, 1992, 2001).
The nature of Twitter retweets, however, rarely allows
bridges to form as retweets that are associated with an
original tweet (unless modified retweets are used). In other
words, the spread of retweets remains within a single step
away from the author who posted that message. Therefore,
this additional type of structural characteristic is not enough
to characterize a new type of influential user in viral adver -
tising. Conceptualizing a second type of parasocial relation-
ship on the network—mentions (the inclusion of a reference
to another Twitter user in a post)—as links in a network
allows bridges to form as they provide an additional connec-
tion among users. While mentions do not represent primary
stages in content distribution, they do provide meaningful
points of context that allow researchers to better understand
the overall virality of an advertisement.
Since content distribution or virality on Twitter does not
take place in a vacuum but rather is often responsive to the
broader online conversion, the distribution of any specific
tweet may be impacted by contextual factors. For example,
the distribution of a tweet about a pharmaceutical company
may be impacted by related actors linked to the industry in
news coverage. On Twitter, users often provide context to
their posted content, among others, by mentioning related
users via their handles (@). While such users do not take an
active role in the conversation, they are nominated, so to
speak, as influencers in the network, as they provide addi -
tional explanation for content virality. In other words, they
allow researchers and practitioners to understand that the vast
distribution of an ad on Twitter is driven by a larger context.

We therefore define a second type of social networks-
driven influencer type as Contextual Influencer—highly
mentioned users who bridge otherwise separated groups of
retweeting users.
RQ3: Which users serve as Contextual Influencers in viral
advertising networks?
Beyond a few users in key positions—Hubs or Bridges—
many users’ content sharing is more limited. Each user con-
tributes little to advertisement virality, as they reach only
their immediate Twitter followers. However, as such users
are often the majority of distribution agents, they ultimately
make a major contribution to ad virality. We call users who
are isolated in the network (defined as incurring no retweets
for their shared video tweets) Low Influence users.
RQ4: What percentage do Low Influencers make of all
users in the network?
Proof of Concept: Heineken’s “Worlds
Apart” Viral Advertisement
To illustrate the conceptual framework proposed in the cur-
rent study, we selected a popular Heineken advertisement on
YouTube, titled “Heineken | Worlds Apart | #OpenYourWorld.”
Heineken described the ad as, “Heineken presents ‘Worlds
Apart’ An Experiment. Can two strangers with opposing
views prove that there’s more that unites than divides us?” In
this ad, Heineken harnesses a social issue, political and social
polarization, and the importance of a constructive conversa-

tion across opinions and ideologies. This campaign received
accolades from the advertising industry and popular press, as
it was compared to a Pepsi campaign that drew on similar
social themes but failed to resonate with social media audi -
ences (Al-Sa’afin, 2017). The video was posted on April 24,
2017, and attracted almost 15 million views by September
30, 2017. This advertisement became viral via a range of
platforms, including Twitter. The advertisement was selected
for this study for its high degree of virality (AdAge.com,
2017).
Method
Data
We used the social media analytics and library platform
Crimson Hexagon to capture all Tweets that included the
URL to the YouTube video ad (https://www.youtube.com/
watch?v=_yyDUOw-BlM). Crimson Hexagon is a Twitter
Certified social media data analysis archive, and collects all
publicly available tweets directly from the Twitter “fire-
house.” The data collected for this study capture all public
Twitter posts that used the hyperlink to the ad in question
(including shortened hyperlinks). We captured all 18,942
tweets posted by 13,009 users between April 20, 2017, when
the video was posted, and September 20, 2017. We elected
for a longer period of data collection time, due to the fact
that viral advertising content often results in mainstream
and trade media (Wallsten, 2010). Furthermore, the explor-
atory nature of this study required a more inclusive data
collection period to account for unexpected waves of
engagement (see Figure 1).
Note that the users @Youtube and @Heineken were
removed from the network data analysis as these handles
were automatically added to any tweet shared from YouTube,

and therefore created artificial connections between all
tweets, potentially misinforming the analysis.
The Network
A customized application was used to extract the retweets,
mentions, and replies relationships from the list of tweets;
the 5,765 retweets relationships; and the 7,212 mentions ties
(including 392 replies, which serve the same function of
appearing on a target user wall). The treatment of mentions
and replies as a single link-type is a common practice in
Twitter network analysis (Isa & Himelboim, 2018; Lee,
Yoon, Smith, Park, & Park, 2017; Yep, Brown, Fagliarone, &
Shulman, 2017). The MS Excel add-on network analysis
application, NodeXL, was used to calculate user- and net-
work-level analysis, as well as for visualization.
For each user in the network, two types of centrality
measurements were calculated, using NodeXL. In-degree
centrality was measured as the number of connections initi-
ated with a given actor (Wasserman & Faust, 1994). On
Twitter, in-degree centrality is based on ties or relationships
that others have initiated with a user (e.g., the number of
users who have retweeted or mentioned that specific user).
Users with the highest values in this metric can be consid-
ered Hubs, highlighting users who have successfully gained
attention to their messages. We determined the cutoff point
for identifying hubs by plotting the distribution of in-degree
by number of users and the drop point in the scree plot-like
graph. Betweenness centrality measures the extent that the
actor falls on the shortest path between other pairs of actors
in the network (Wasserman & Faust, 1994). The more
people depend on a user to make connections with other
Figure 1. Twitter activity of posts including a hyperlink to the
Heineken viral advertisement.

https://www.youtube.com/watch?v=_yyDUOw-BlM
https://www.youtube.com/watch?v=_yyDUOw-BlM
people, the higher that user’s betweenness centrality value
becomes. This value is therefore associated with Bridges in
a network.
Findings
The study identified a total of 13,009 users who posted a link
to the original Heineken advertiseme nt on YouTube. With
almost 15 million views of this video on YouTube, this num-
ber of tweets may appear low. However, each tweet posted
by a user reaches all its Twitter followers. A message’s poten-
tial reach or impressions are therefore calculated as the total
number of Twitter walls, or user accounts, on which these
tweets appeared. This metric is calculated by adding up all
followers of all users who posted an original tweet with the
advertisement URL and the followers of all users who
retweeted such posts (Sterne, 2010). For the 13,009 users,
the total reach was 48,962,936 users (the sum followers of all
users in the network), meaning that for almost 50 million
users, this advertisement appeared on their own Twitter
pages. While it does not necessarily mean that the users all
saw the ad, or even the link to it, and it does not take dupli -
cates into account, the high reach value illustrates the poten-
tial advertising distribution of the 13,009 tweets.
Characteristics and Viral Advertising—Time
The vast majority of advertising spread on Twitter (16,152
tweets, 85.27%) were posted within the first 2½ weeks fol -

lowing the date the video was posted (April 20, 2017—May
6, 2017). The remaining posts were spread over the remain-
ing 4.5 months of data collection. Notably, within the first
5 days of the video’s publication, only 248 tweets linked to
it. This relatively low-engagement period was followed by
highly retweeted activity of individual users such as @Cait_
Kahle, a consumer public relations professional, who
tweeted on April 26, “Incredible perspective from @
Heineken via an advertisement #OpenYourWorld: https://t
.co/ApmYwteLwn.” This tweet gained 463 retweets. @
CaseyNeistat, a photographer, posted on April 27, “y’all see
that heineken commercial yet? it should win ALL ad
awards—https://t.co/gFDXwy7F31,” gaining 1,172 retweets
(see Figure 1).
Characteristics of Viral Advertising—Distribution
of Spread and Reach
RQ1: What is the distribution structure of a viral adver-
tisement on Twitter?
The distribution histogram of tweets was found to be
highly skewed. Of the 7,422 users who posted an original
tweet with a link to the advertising video on YouTube, 5,875
received no engagement from others (79.19% of original
tweets and 45.16% of total tweets). These are isolated users
in the network in terms of advertising spread. For 933 users,
only one retweet occurred (i.e., two users spread); 214 users
were retweeted by two users, 214 by three users, 75 by four,
and 29 users had five retweets each. At the other end of this
skewed distribution, a few single tweets were shared many
times (1,154; 435; 336; 310; and 102 retweets for each of
the top five most shared users). Figure 2 illustrates the
distribution.

RQ2: Which users serve as Hubs in a viral advertising
network?
The top users, those who posted the most retweeted tweets
linking to the Heineken advertisement video, were primarily
individuals with no affiliation to the brand: @CaseyNeistat
(Casey Neistat), a videographer (1,214 retweets); Cait_Kahle
(Caitlin Kahle), a consumer public relations professional
(465 retweets); @ChrisRGun (The Cuntacular Chris), a user
who posts political jokes and videos (99 retweets); @jrisco
(Javier Risco), a Mexican journalist (96 retweets); @
MaverickGamersX (Maverick Gamers), an aggregator for
video game/film/entertainment industry news and reviews
(88 retweets); @willwillynash (Will Nash), a director of
short films and music videos (70 retweets); @anmintei, a
professor at the University of Tokyo (53 retweets); @
TheSeanODonnell (Sean O’Donnell), an actor and producer
(49 retweets); @COPicard2017, a fan account for Jean-Luc
Picard (42 retweets); and @rands, a vice president at Slack
(42 retweets).
Characteristics of Viral Advertising—The network
RQ3: Which users serve as bridges in viral advertising
networks?
Social network analysis maps and examines patterns of
spread of tweets across Twitter users. However, the network
of retweets will create a set of disconnected silos, because
Figure 2. The histogram of URL spread by Twitter users.
Both axes are in logarithmic scales.
https://t.co/ApmYwteLwn
https://t.co/ApmYwteLwn
https://t.co/gFDXwy7F31

any retweet of a post will be attributed to the original tweet.
Understanding the network of social ties beyond retweets
reveals the cross-silo interactions. We therefore expanded
the dataset to include not only retweets but also mentions and
replies within the set of 13,009 users who posted content
with the ad’s URL.
Figure 3 illustrates the social networks created when using
only retweets as links. For illustration purposes, only clusters
created by the most retweeted users were included. Each
highlighted user is one of the top retweeted users, surrounded
by users who retweeted their post’s link to Heineken’s adver -
tisement on YouTube. Clearly, such a network does not pro-
vide more information than was previously gained from
identifying the top retweeted posts. The hubs were discussed
in an earlier analysis. This figure highlights the limitation of
examining retweets as the sole relationships in Twitter activity
surrounding this viral advertisement.
Adding mention relationships into the network adds
another layer of connectivity. The retweeting clusters are no
longer siloed and new clusters are formed. The top between-
ness user added in this network is @Pepsi (in-degree = 352;
betweenness centrality = 1,851,055.41).
Examining the content that included @Pepsi reveals an
important theme of the tweets helping to spread the Heineken
ad: the controversial Pepsi advertisement criticized for cultural
and racial insensitivities. In fact, 315 of the tweets mentioning
@Pepsi and linking to the Heineken ad were not retweets and
were not retweeted, and would have otherwise been potentially
ignored, as they “failed” to attract retweets. The other cluster

contained two additional bridges in the network: @Heineken_
UK (in-degree = 181; betweenness centrality = 730,191.83) and
@publicislondon, a London advertising agency (in-degree =
63; betweenness = 740,877.09). These two users gained no
meaningful retweets, and therefore did not reach the surface of
the initial retweets analysis. However, they were highly men-
tioned by users who posted links to the advertisement, contrib-
uting to its virality. This finding highlights the potential
significance of advertising agencies in the distribution of a viral
ad on Twitter beyond their ability to inspire retweets. This anal -
ysis points to the strategic application of the agencies’ Twitter
networks as distribution mechanisms.
Figure 4 illustrates the connectivity power of mentions,
allowing for more in-depth understanding of the viral distri-
bution process. Specifically, we can see the siloed clusters
created by highly retweeted users, and the connections cre-
ated by highly mentioned users.
RQ4: What percentage do Low Influencers make of all
users in the network?
Of the total 13,009 users who shared a link to the Heineken
advertisement, 5,875 (45.16%) were not retweeted even one
time, making them isolated in the network. In other words,
while each of these users did not make a major contribution
to the virality of the ad, as a whole, through comprising
almost half of the users, they did make a major contribution,
thus supporting the idea of Low Influence users as important
for viral advertising. The total potential reach of these Low
Influence users, calculated as the sum of the number of their
followers, is 9,091,133, 18.56% of the total potential reach
(48,962,936) of all users in the network.
Discussion

The current study aims to advance social media virality
and social media influencers in advertising scholarship by
Figure 3. The retweets-based social network of Heineken’s viral
advertisement.
incorporating a social networks approach. We propose and
empirically identify three distinct types of social media
influencers and thus highlight the multifaceted nature of dis-
tribution in viral advertising. Using the Heineken’s Worlds
Apart ad for proof of concept, we identified three types of
key users, based on their network connectivity: Primary
Influencers (retweeted hubs), Contextual Influencers
(bridges), and Low Influencers (network isolates). As men-
tioned, viral advertising largely depends on audience partici -
pation in content distribution. Our analysis highlights the
distinct role of each of the three influencers in ad distribu-
tion. The current study aims to advance the understanding of
the viral advertising process by offering a more macro-view
of advertisements on Twitter. This view broadens the analy-
sis of ad distribution beyond the single peer-to-peer flow,
allowing for a multi-step structure available only through
network analysis. Moving away from the question of what
makes an advertising viral and toward the question of who
makes an advertising viral, our study points to a highly
skewed nature of distribution. The results of our analysis
indicate that a small number of users disproportionately con-
tributed to the distribution of the ad on Twitter, while the vast
majority of users made a more modest contribution individ-
ually, but a major contribution as a whole.

The Skewed Distribution of Primary Influence
One unique characteristic of viral advertising on Twitter is
the multiple points of origin for an advertisement video.
While the ad has a single source, often YouTube or the brand
website, it starts a potential cascade of sharing on Twitter via
multiple tweets. This study demonstrates the differential
contribution of individual Twitter accounts, either affiliated
or not affiliated with a brand. Consistent with previous scho-
larship on viral advertising (Petrescu, 2014; Phelps et al.,
2004) and information sharing on Twitter (Araujo et al.,
2017), we found that a small number of users had more influ-
ence on content distribution than most. Scholarship often
points to celebrities, elites, and media organizations as key
influencers due to their large Twitter following (Himelboim
et al., 2014; Jin & Phua, 2014). The current study points to a
more nuanced explanation.
Taking a social networks approach, the structure of inter-
actions created by acts of sharing only (i.e., retweets) fails to
explain the overall structure of information flow in the net-
work in two main ways. First, the major clusters of retweets
remain isolated. Such a siloed structure cannot explain the
virality of advertising beyond a set of a few highly shared
tweets. Second, the approach ignores the majority of users
who made much more moderate contributions to the virality
of the ad, as they received little or no retweets.
Bridges: The Contextual Influencers
Viral advertising does not take place in vacuum and content
sharing takes place in a broader conversational context.
A second type of social media influencers —Contextual
Influencers—are those who conceptualize and operationalize

Figure 4. The retweets and mentioned-based social network of
Heineken’s viral advertisement.
as users who do not necessarily take an active part in the con-
versation but are brought into the chatter by users who men-
tioned them, as they contribute to advertising virality by
sharing a link to an ad. Social media influencers are often
characterized by the amount of social connections they have,
in terms of followers or subscribers (Abidin, 2016). Contextual
influencers are defined by Twitter mentions, a unique type of
parasocial relationship that captures best the idea of a sense of
engagement of audience members with on-screen characters
(Tukachinsky, 2010).
Building upon literature about Bridges and structural
holes, highly mentioned users play a role by symbolically
connecting many users who were not retweeted, based on
parasocial relationships that they form in the network. In
other words, introducing both a new type of ties, mentions of
users as links, and an additional node-level structural net-
work position, a bridge provides context for understanding
certain mechanisms in the distribution of viral advertising
that have been ignored by recent scholarship, which has
focused solely on highly retweeted users.
Contextual influencers, in the mention-based clusters of
low engagement users, provide the context and a possible
explanation for the advertisement’s virality. In the case study,
a common reason for posting the video was in comparison
with a failed competing campaign (Pepsi) and in the context
of the advertising agency behind that campaign, Publicis
London (Publicis, 2017). As one may recall, both Pepsi and

Heineken launched campaigns aiming to gain advertising
distribution through the production of socially provocative
advertisements. While Pepsi was often criticized for its cam-
paign, Heineken was praised. Even though Pepsi did not take
an active part in distributing the Heineken ad, as it never
posted a link to it, Pepsi is an influencer in the network
because it influenced the virality of the Heineken ad. It is the
comparison between the two campaigns that people posted
about, when they distributed the Heineken ad link.
Further analysis of patterns of mentions among users in
examples of viral advertising spread on Twitter can provide
researchers and practitioners with an understandi ng of the
triggers of distributing advertising content on Twitter. In
other words, this proposed methodology shifts the focus
from aiming to explain the reason for high levels of
retweets, to explaining the reasons for high level of posts,
even if these posts received no engagement. It should be
noted that while @HeinekenUK appeared together in a
cluster with @PublicisLondon, it had limited influence net-
work connectivity.
Isolates: The Influence of the Low Influence Users
Expanding the breadth of the network to include mentions
as network links also revealed clusters of users who contrib-
uted to the virality of the advertisement but in a more hidden
manner, as they did not attract many retweets. However,
these users vastly contributed to the reach of the ad on
Twitter as whole. In the Heineken case, almost four of five
original tweets posted with the Heineken video were not
retweeted, representing about 45% of all tweets in our data-
set. At face value, the contribution from each of these users
seems minimal. However, when aggregated, we find that
these seemingly non-successful content distributors played

an important role in the overall distribution of the advertise -
ment. Furthermore, the video appeared on the walls of all of
their followers, even if they were not retweeted, expanding
their contribution to the virality even further. In the Heineken
case, they accounted for almost a fifth of the total potential
reach. Considering Nielsen’s (2006) idea of the 1-9-90 rule,
findings suggest that within the majority of seemly non-
influential users, many have limited influence on advertis-
ing distribution. But when aggregated, these users make a
major impact on advertising virality.
There is no doubt that influencer analysis is an important
factor in understanding viral distribution of Internet content.
As made evident by previous studies, not all Twitter users are
equal in their ability to promote content, with elites, corpora-
tions, and celebrities often leading the discussion. The results
of our analysis point to an often-overlooked phenomenon, the
influence of non-influencers, a phenomenon that occurs when
individuals fail to meaningfully contribute to the structure of
a network, but play an important role in shaping the netw ork
when grouped with other non-influencers, ultimately making
a meaningful contribution to viral advertising.
Conclusions and Limitations
Recognizing the potential of the network approach in
informing and advancing research on viral advertising, our
study demonstrated the multi-level ad distribution process.
Whereas most previous studies focused on what may lead to
advertisement virality (Chu, 2011; Golan & Zaidner, 2008;
Hayes & King, 2014), our study addresses another impor-
tant question: who makes advertising viral? We identified
three key types of influencers. The first are the highly
retweeted users in the network, each individually makes a
major contribution to the distribution of ads. The second are
highly mentioned users who make a crucial yet passive con-

tribution to content virality. These serve as Bridges, filling
structural holes left in the retweets-only networks. Third are
Low Influencers, who each introduced the ad to Twitter by
posting an original tweet with a link. Individually their con-
tribution to ad virality does not go beyond their group of
followers; however, their aggregated influence on virality is
vast, making them influential in the network.
Finally, the current study advances the methodological
approach to the study of viral advertising. Network analysis
is the only method that allows for a meaningful representa-
tion of the viral advertising distribution process. The defini -
tion of an advertisement as a single paid form of media
requires a third approach where data are collected based
on a single piece of content, namely, a hyperlink to an
advertisement. Collecting Twitter data based on a single
URL results in a dataset that captures the spread of a single
ad across a range of distributors, as opposed to the traditional
data collection strategy based on a set of keywords capturing
a conversation about a brand. We argue this strategy is not
only unique to viral advertisement, but is the most appropri -
ate strategy overall. These conceptual and methodological
contributions are applicable beyond the study of viral adver -
tising and the field of marketing, as social media influencers
play a key function of content distribution on online plat-
forms with implications for scholars and practitioners alike
across discipline.
The proposed conceptual framework had the key limita-
tion of testing only a single dataset. Future studies should
apply this network approach across viral advertising cam-

paigns and across a range of brands. Similarities and differ-
ences in the two types of influential users proposed here
could lead to better understanding of the nature of viral
advertising on social media. In the same vein, our analysis of
the Pepsi account as a key bridge evidenced the potential
contribution of non-affiliated accounts to the overall viral
network. Our study did not examine other key users, and thus
did not account for their potential influence. Furthermore,
any study about engagement is susceptible to a bias made by
fake engagement, an ongoing issue for researchers and prac-
titioners (Pathak, 2017).
The current study is also limited by its use of a single case
study on a single social media platform. Social media con-
tent is almost never distributed via a single platform alone,
but rather becomes viral through the integration and distribu-
tion of content across platforms as individuals share links to
content in multiple ways. We recognize this issue as a limita-
tion of our study and call upon future studies to consider this
consideration in their design.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with
respect
to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research,
author-
ship, and/or publication of this article.
ORCID iD
Itai Himelboim https://orcid.org/0000-0001-7981-5613

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Itai Himelboim (PhD, University of Minnesota) is an associate
pro-
fessor of Advertising and Public Relations and the director of
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Grady College of Journalism and Mass Communication, of the
University of Georgia. His research interests include social
media
analytics and network analysis of large social media activity
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Guy J. Golan (PhD, University of Florida) is the director of the
Center for Media & Public Opinion. He has published more than
40
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Question
What was the relationship between big business and the courts
prior to the Wagner act? How did laws and the courts affect
labor relations/unions and business?

There was a crucial relationship between courts and big
business prior to Wagner Act. The objective of Wagner Act was
to establish the authorized right of most employees to join labor
unions and to giveaway jointly with their workers (epi, 2021). It
is restricted organizations from getting engaged in unfai r labor
rehearses. Before Wagner Act associations between companies
and employees were unsure and combative. Conspiracy ideology
is created as an attempt for making unions unauthorized for
arguing the creation of hostile atmosphere and harming the
privileges of companies (Johnston, 2018). [I am not finding this
conspiracy and wagner act relationship in the Johnston source.]
Wagner Act designed administration-regulated National Labor
Relations Board to assure businesses and companies that don’t
hinder or interfere unionization in organizations. NLRB is used
as a tool by arbitrating clashes between organizations and
employees. Majority of workers support union that might
capable bargaining agent by forcing workers for negotiating
terms of job with selected representatives of workers.
Discharging the workers consider unfair practices in workplace
(Whitham, 2020). I cannot verify this source. Google Scholar
requires me to buy it. Further, it is not found in NU’s system.
The relationship between court laws and big businesses
creates hostile environment in job. The companies hinder
unionization in environment of working. Courts and laws
influence labor associations in workers that relish unionized
privileges, where firm join and form improvement in conditions
of working by bargaining the representatives (Badas, 2019). I
read the Badas source and am not getting the same message.
Thus the post is off topic from the message in the source.
Moreover, unionized labor brings improvement in conditions of
working that is also risky for firms. NLRA also secured rights
of humans, and it is the function of unions to limit various
employees in organization to increase wages. Similarly, the
unions have destructed employment in firm by enhanced
investment in business. Where is the source to support this fact
about the NLRA?

There is an important association between management and
labor, as it grows the development of contract of job on
equitable and sound basis. Procedures can be delivered in order
for determining whether there is representation of workers that
aim to remove main reasons of wasteful fiscal conflict
(Attitudes towards Behavior, 2021). Harmful practices can be
prevented that might be harmful for independence of labor and
to find every employee in scope with independence of action
and choice. Moreover, courts and laws influence affect labor
relations/unions and business. NLRA gives guarantee gathered-
bargaining privileges (Biasi, 2018).[this source didn’t say
anything about the NLRA and guarantees. It argues self
employment/gig workers issues. It also out-ruled the practice
of blacklisting union forerunners.
There is a crucial influence of labor laws on wages of workers
and expenditures of company. Union might decrease inequality
in salary and might enhance salaries more or middle and lower
wage employees as compared higher salary workers as
compared to white collar employees. Wagner Act shows that
federal administration is designed to move against companies to
strengthen the privileges of labor to bargain and unionize
collectively but it does not execute no mutual obligations on
union (Gebert, 2021). NLRB includes unfair practices of labor
as well as decision-making that the courts might review. Union
also limits various employees and then drive then remaining
salaries of workers. See next page.
Garfield, this work as some of the same problems that I
originally addressed, which is choice of sentence structure and
organization makes the reading hard to follow and the sources
do not match the message in your writing. Some sources are un
verifiable.
Grade will remain a zero for this assignment. If you would like
to use the writing center to help you improve the message, AND
clean up the sourcing with the librarians, I will reconsider it.
Due by the last day of the class. Must have proof you are
working with the writing center.

V/R Prof. Reaves
References
Attitudes towards Behavior (2021). General attitude toward
unions Missing source information.
Johnston, H. (2018). Workplace gains beyond the Wagner Act:
The New York taxi workers alliance and participation in
administrative rulemaking. Labor Studies Journal, 43(2), 141-
165.
Biasi, M. (2018). ‘We will all laugh at gilded butterflies’. The
shadow of antitrust law on the collective negotiation of fair fees
for self-employed workers. European Labour Law Journal, 9(4),
354-373.
Badas, A. (2019). Policy disagreement and judicial legitimacy:
Evidence from the 1937 Court-Packing plan. The Journal of
Legal Studies, 48(2), 377-408.
Whitham, C. (2020). The War Before the War, 1933–1939.
In Corporate Conservatives Go to War (pp. 23-40). Palgrave
Macmillan, Cham.
Gebert, R. (2021). The pitfalls and promises of successfully
organizing Foodora couriers in Toronto. In A Modern Guide To
Labour and the Platform Economy. Edward Elgar Publishing.
epi. (2021). Workers shouldn’t have to depend on tips to
survive. Retrieved from epi: https://www.epi.org/
The Wagner Act on Industry and Employees
The Wagner Act, also called the National Labor Relations Act
of 1935, is the most notable law influential in the 20th century
regarding employer-employee relationships (Lassabe, 2021).
The purpose of the law was to ensure employees' civil rights by
forming trade unions for negotiating the employment terms with
the companies jointly. However, the law excluded agricultural
and domestic workers. However, the Wagner Act has helped the
labour and unions, shifting the dynamic between employee
unions and the federal government (Morgan, 2019). The Act

successfully secured the employees' rights, safeguarding their
union membership. The Act fostered the formation of the
National Labor Relations Board (NLRB), which was
government-supervised to avoid conflicts between the
employees and employers during unionization in the work
environments. The statute helped arbitrate employee-employer
disagreements in organizations.
Following the Wagner Act, employees' majority support for the
union could enable a sole bargaining agent, forcing employers
to negotiate employment terms with chosen employees'
representatives. The employment officers in firms that lack
collective agreements tend to be very careful in creating
awareness of compensation and working conditions for the
employees in these work relationships. Individual employees'
rights and privileges are also crucial on these grounds, along
with a clear understanding of the organization's policies
concerning employer-employee relationships. According to the
Wagner Act, discharging an employee who is a labor union
member is considered an unfair workplace practice (De Roeck &
Farooq, 2018). Through collective bargaining sessions to
negotiate the issues, the Wagner Act and collective bargaining
approaches have reduced the issues of sixteen-hour workdays,
harmful equipment, unhealthy working environments, and child
labor. The Act has also promoted equal pay and ethical
treatment of workers as the top concerns in labor relations.
Other issues that are raising the government's concern in
employment relationships are job protection and employee pay
equity (Morgan, 2019).The employee unions have focused on
health care, short working weeks, employee training, and
affirmative actions highlighted in the collective bargaining
sessions.
Before developing the Magner Act, the relationship between big
businesses and court laws showed a combative and unsure
approach. The ties created a conspiracy doctrine that made the
unions illegal, claiming that they were the source of a hostile
work environment. At that time, businesses hindered

unionization in work environments. However, the laws and
courts affected labor relations in that employees enjoyed
unionization rights, where the organization formed and joined
labor organizations through collective bargaining through
representation. The unionized labor improved working
conditions, employees' livelihoods, and challenged
organizations. Laws like the NLRA have protected human rights
(Lessabe, 2021). The unions have functioned like cartels to
restrict the number of workers within an organization to raise
pay. At the same time, the unions have destroyed jobs in
organizations through increased business investment.
References
De Roeck, K., & Farooq, O. (2018). Corporate social
responsibility and ethical leadership: Investigating their
interactive effect on employees’ socially responsible
behaviors. Journal of Business Ethics, 151(4), 923-939.
Lassabe, L. (2021). Divided unions: the Wagner act, federalism,
and organized labor.
Morgan, J. (2019). Will we work in twenty-first century
capitalism? A critique of the fourth industrial revolution
literature. Economy and Society, 48(3), 371-398.
www.elsevier.com/locate/intmar
Available online at www.sciencedirect.com
ScienceDirect
Journal of Interactive Marketing 28 (2014) 43–54
Consumer Decision-making Processes in Mobile Viral
Marketing Campaigns
Christian Pescher & Philipp Reichhart & Martin Spann ⁎
Institute of Electronic Commerce and Digital Markets, Ludwig-

Maximilians-Universität München, Geschwister-Scholl-Platz 1,
D-80539 München, Germany
Available online 22 November 2013
Abstract
The high penetration of cell phones in today's global
environment offers a wide range of promising mobile marketing
activities, including
mobile viral marketing campaigns. However, the success of
these campaigns, which remains unexplored, depends on the
consumers' willingness to
actively forward the advertisements that they receive to
acquaintances, e.g., to make mobile referrals. Therefore, it is
important to identify and
understand the factors that influence consumer referral behavior
via mobile devices. The authors analyze a three-stage model of
consumer referral
behavior via mobile devices in a field study of a firm-created
mobile viral marketing campaign. The findings suggest that
consumers who place
high importance on the purposive value and entertainment value
of a message are likely to enter the interest and referral stages.
Accounting for
consumers' egocentric social networks, we find that tie strength
has a negative influence on the reading and decision to refer
stages and that degree
centrality has no influence on the decision-making process.
© 2013 Direct Marketing Educational Foundation, Inc.
Published by Elsevier Inc. All rights reserved.
Keywords: Mobile commerce; Referral behavior; Sociometric
indicators; Mobile viral marketing
Introduction
The effectiveness of traditional marketing tools appears to be

diminishing as consumers often perceive advertising to be
irrelevant or simply overwhelming in quantity (Porter and
Golan 2006). Therefore, viral marketing campaigns may provide
an efficient alternative for transmitting advertising messages to
consumers, a claim supported by the increasing number of
successful viral marketing campaigns in recent years. One
famous example of a viral marketing campaign is Hotmail,
which acquired more than 12 million customers in less than
18 months via a small message attached at the end of each
outgoing mail from a Hotmail account informing consumers
about the free Hotmail service (Krishnamurthy 2001). In
addition
to Hotmail, several other companies, such as the National
Broadcasting Company (NBC) and Proctor & Gamble, have
successfully launched viral marketing campaigns (Godes and
Mayzlin 2009).
In general, a viral marketing campaign is initiated by a firm that
actively sends a stimulus to selected or unselected consumers.
However, after this initial seeding, the viral marketing
campaign
⁎ Corresponding author.
E-mail addresses: [email protected] (C. Pescher),
[email protected] (P. Reichhart), [email protected] (M. Spann).
1094-9968/$ -see front matter © 2013 Direct Marketing
Educational Foundat
http://dx.doi.org/10.1016/j.intmar.2013.08.001
ion, In
relies on peer-to-peer communications for its successful
diffusion among potential customers. Therefore, viral market-
ing campaigns build on the idea that consumers attribute higher
credibility to information received from other consumers via
referrals than to information received via traditional advertising
(Godes and Mayzlin 2005). Thus, the success of viral marketing

campaigns requires that consumers value the message that they
receive and actively forward it to other consumers within their
social networks.
Mobile devices such as cell phones enhance consumers'
ability to quickly, easily and electronically exchange informa-
tion about products and to receive mobile advertisements
immediately at any time and in any location (e.g., using mobile
text message ads) (Drossos et al. 2007). As cell phones have the
potential to reach most consumers due to their high penetration
rate (cf., EITO 2010), they appear to be well suited for viral
marketing campaigns. As a result, an increasing number of
companies are using mobile devices for marketing activities.
Research on mobile marketing has thus far devoted limited
attention to viral marketing campaigns, particularly with respect
to
the decision-making process of consumer referral behavior for
mobile viral marketing campaigns, e.g., via mobile text
messages.
Thus, the factors that influence this process remain largely
unexplored. The literature on consumer decision-making
suggests
that consumers undergo a multi-stage process after receiving a
c. Published by Elsevier Inc. All rights reserved.
http://crossmark.crossref.org/dialog/?doi=10.1016%2Fj.intmar.2
013.08.001&domain=pdf&date_stamp=2014-02-01

44 C. Pescher et al. / Journal of Interactive Marketing 28 (2014)
43–54
stimulus (e.g., a mobile text message) and before taking action
(e.g., forwarding the text message to friends) (Bettman 1979;
De
Bruyn and Lilien 2008). At different stages of the process,
various
factors that influence consumer decision-making can be
measured
using psychographic, sociometric, and demographic variables as
well as by consumer usage characteristics. Whereas previous
studies have mainly focused on selected dimensions, our study
considers variables from all categories.
De Bruyn and Lilien (2008) analyzed viral marketing in an
online environment and discussed relational indicators of
business students who had received unsolicited e-mails from
friends. This study provided an important contribution and
amplified our understanding about how viral campaigns work.
The present paper differs from the work of De Bruyn and Lilien
(2008) and goes beyond their findings in four important ways:
actor, medium, setting, and consumer characteristics. The first
difference is the actor involved. In viral campaigns, the
initiator, usually a company, sends the message to the seeding
points (first level). Next, the seeding points forward the
message to their contacts (second level), and so on. Whereas
De Bruyn and Lilien (2008) focused on the second-level actors,
the present study focuses on the first-level actors, e.g., the
direct
contacts of the company. We believe that for the success of a
campaign, additional insights into the behavior of first-level
actors are very important because if they do not forward the
message, it will never reach the second-level actors. The second
difference is the medium used in the campaign. Although we
cannot explicitly rule out that participants of De Bruyn and

Lilien's (2008) campaign used mobile devices, they conducted
their campaign at a time when the use of the Internet via mobile
devices was still very uncommon. Therefore, it is reasonable to
assume that at least the majority of their participants used a
desktop or a laptop computer when they participated in De
Bruyn and Lilien's (2008) campaign. In contrast, the present
study explicitly uses only text messages to mobile devices. In
addition, mobile phones are a very personal media which is
used in a more active way compared to desktop or laptop
computers (Bacile, Ye, and Swilley 2014). The third difference
is
the setting in which the viral campaign takes place. Whereas the
participants in the study by De Bruyn and Lilien (2008) were
business students from a northeastern US university, we conduct
a mobile marketing campaign in a field setting using randomly
selected customers. The fourth and most important difference is
that De Bruyn and Lilien (2008) focused exclusively on
relational
characteristics. In addition to relational characteristics, this
paper also considers variables that describe demographic
factors,
psychographic factors, and usage characteristics. As these
variables yield significant results, the study and its findings go
beyond the findings of De Bruyn and Lilien (2008).
The main goal and contribution of this work is, first, to
analyze consumers' decision-making processes regarding their
forwarding behavior in response to mobile advertising via their
cell phone (i.e., text messages) in a mobile environment using a
real-world field study. To analyze consumers' decision-making
processes, we use a three-stage sequential response model of
the consumer decision-making process. Additionally, we inte-
grate consumers' egocentric social networks into a theoretical
framework to consider social relationships (e.g., tie strength,
degree centrality) when analyzing mobile viral marketing
campaigns. Thus, to understand referral behavior, we integrate

psychographic (e.g., usage intensity) and sociometric (e.g., tie
strength) indicators of consumer characteristics. We are then
able
to determine the factors that influence a consumer's decision to
refer a mobile stimulus and are able to identify the factors that
lead to reading the advertising message and to the decision to
learn more about the product.
Related Literature
Viral Marketing and Factors that Influence Consumer Referral
Behavior
Viral marketing campaigns focus on the information spread of
customers, that is, their referral behavior regarding information
or
an advertisement. Companies are interested in cost-effective
marketing campaigns that perform well. Viral marketing cam-
paigns aim to meet these two goals and can, accordingly, have a
positive influence on company performance (Godes and Mayzlin
2009). Companies can spread a marketing message with the
objective of encouraging customers to forward the message to
their
contacts (e.g., friends or acquaintances) (Van der Lans et al.
2010).
In this way, the company then benefits from referrals among
consumers (Porter and Golan 2006). Referrals that result from a
viral marketing campaign attract new customers who are likely
to
be more loyal and, therefore, more profitable than customers
acquired through regular marketing investments (Trusov,
Bucklin,
and Pauwels 2009).
Two streams of research can be identified. The first is the
influence of viral marketing on consumers, and the second is

research that has analyzed the factors that lead to participating
in viral marketing campaigns. First, previous research identified
that viral marketing influences consumer preferences and pur-
chase decisions (East, Hammond, and Lomax 2008). Further, an
influence on the pre-purchase attitudes was identified by Herr,
Kardes, and Kim (1991). In addition, viral marketing also
influences the post-usage perceptions of products (Bone 1995).
Second, previous research has identified satisfaction,
customer commitment and product-related aspects as the most
important reasons for participating in viral marketing campaigns
(cf., Bowman and Narayandas 2001; De Matos and Rossi 2008;
Maxham and Netemeyer 2002; Moldovan, Goldenberg, and
Chattopadhyay 2011). With respect to psychological motives,
self-enhancement was identified as a motive for consumers to
generate referrals (De Angelis et al. 2012; Wojnicki and Godes
2008). The importance of self-enhancement in addition to social
benefits, economic incentives and concern for others was
identified
as a motive behind making online referrals (Hennig-Thurau et
al.
2004). Referrals can be differentiated into positive and negative
referrals. Anxiety reduction, advice seeking and vengeance are
factors that contribute to negative referrals (Sundaram, Mitra,
and
Webster 1998).
Within the referral process, the relationships and social
network position of the consumer are also influential. For
example, Bampo et al. (2008) found that network structure is
45C. Pescher et al. / Journal of Interactive Marketing 28 (2014)
43–54
important in viral marketing campaigns. Furthermore, it has

been determined that consumers are more likely to activate
strong ties than weak ties when actively searching for
information (Brown and Reingen 1987) because strong ties
tend to be high-quality relationships (Bian 1997; Portes 1998).
In addition, targeting consumers who have a high degree
centrality (e.g., quantity of relationships) leads to a higher
number of visible actions, such as page visits, than do random
seeding strategies (Hinz et al. 2011). Kleijnen et al. (2009)
analyzed the intention to use mobile services using sociometric
variables and evaluated how consumers' network positions
influence their intentions to use mobile services. However, the
previous study contributes to the literature by analyzing a
different
research question than is examined in our paper. Specifically,
Kleijnen et al. (2009) focused on the intention to use services,
while our study focuses on consumers' decision-making
processes
until they make a referral. In summary, previous research
focused
on the consumers' psychographic constructs or relationships and
social networks to explore why consumers participate in viral
marketing campaigns and why they make referrals, two
constructs
that are rarely analyzed together. Iyengar, Van den Bulte, and
Valente (2011) used both constructs jointly and found that
correlations between the two are low. However, this study did
not take place in an online or mobile context but rather in the
context of referrals for new prescription drugs between
specialists.
In contrast, our study analyzes both aspects together within a
mobile viral marketing campaign.
In addition to offline- or online-based viral marketing activities,
an increasing number of companies are conducting marketing
campaigns using mobile phones, and promising approaches
include mobile viral marketing campaigns. Research on mobile

viral marketing is relatively unexplored because most research
in
the field of mobile marketing analyzes marketing activities such
as
mobile couponing (Dickinger and Kleijnen 2008; Reichhart,
Pescher, and Spann 2013), the acceptance of advertising text
messages (Tsang, Ho, and Liang 2004) or the attitudes toward
(Tsang, Ho, and Liang 2004) and the acceptance of mobile
marketing (Sultan, Rohm, and Gao 2009). In the context of
mobile
viral marketing research, Hinz et al. (2011) studied mobile viral
marketing for a mobile phone service provider and determined
that
the most effective seeding strategy for customer acquisition is
to
focus on well-connected individuals. In contrast to our study,
their
referrals were conducted via the Internet (i.e., the companies'
online referral system) rather than via a mobile device (i.e.,
forwarding the text message immediately). Nevertheless,
generat-
ing referrals using a mobile device can affect referral behavior.
Palka, Pousttchi, and Wiedemann (2009) postulated a grounded
theory of mobile viral marketing campaigns and found that trust
and perceived risk are important factors in the viral marketing
process. In comparison to our study, they used qualitative
methods
and did not conduct a real-world field study. Okazaki (2008)
identified, for Japanese adolescents, consumer characteristics
such
as purposive value and entertainment value are the main factors
in mobile viral marketing campaigns and that these factors
significantly influence the adolescents' attitudes toward viral
marketing campaigns. Furthermore, both purposive value
and entertainment value are influenced by the antecedents'
group-person connectivity, commitment to the brand, and

relationship with the mobile device. In contrast to our study,
Okazaki (2008) did not analyze whether referrals were made,
nor did he analyze the referrals that were directly made via a
mobile device by forwarding the mobile text message. Instead,
he
analyzed the general viral effect in the form of telling or
recommending the mobile advertising campaign. Further, our
field study analyzes the entire consumer decision-making
process
for a mobile viral marketing campaign via text messages across
the three stages: from stage one, reading, to stage two, interest,
to
stage three, decision to refer.
To summarize, in contrast to the existing studies in the field
of mobile viral marketing, we analyze consumers' egocentric
networks via measures such as tie strength and degree
centrality. These sociometric factors are analyzed jointly
with psychographic constructs across the three stages in the
decision-making process. Thus, our study uses a real-world
mobile viral marketing campaign and enables us to test the
relative importance of social embeddedness and consumer
characteristics with respect to consumers' decision to forw ard
mobile messages.
Decision-making Process and Specifics of the Mobile
Environment
Consumer decision-making is a multiple-stage process
(Bettman 1979; De Bruyn and Lilien 2008; Lavidge and
Steiner 1961). In a viral marketing campaign, the final goal is
to
generate a high number of referrals. Therefore, our model of
consumer forwarding behavior is designed for the specific
situation of mobile viral marketing campaigns.

The process and first stage begin with the consumer reading
a mobile advertising text message on his or her mobile phone.
If this text message sparks the consumer's interest and the
consumer wants to learn more about the offered product, he/she
enters the interest stage, which is the second stage of the model.
If the consumer finds the product interesting after learning
about it, he or she makes a referral, which is the third stage of
our model (decision to refer).
In this study, we analyze the stages of the consumer
decision-making processes within a mobile environment, i.e.,
within a mobile viral marketing campaign. There are several
differences between mobile viral marketing and online or
offline viral marketing. A mobile text message is more
intrusive than an e-mail because it appears immediately on
the full screen. Consumers usually carry their mobile phone
with them and a mobile message may also reach them in a
private moment. Contrary, consumers may need to purposely
look into their e-mail accounts to receive e-mails. Therefore a
mobile message can be more personal compared to an e-mail.
In comparison to offline face-to-face referrals, mobile referrals
do not possess this personal aspect and can be transmitted
digitally within a few minutes to several friends in different
places simultaneously. This is not possible in the offline world.
Additionally, a mobile referral can reach the recipient faster
than an e-mail or an offline referral. Thus, the mobile device
may influence the referral behavior due to its faster digital
transmission of information.
46 C. Pescher et al. / Journal of Interactive Marketing 28 (2014)
43–54
Development of Hypotheses
While the factors that influence the stages of the decision-

making process can be divided into two groups, we analyze
them jointly in this study. The first group consists of the
psychographic indicators of consumer characteristics, thus
focusing on each consumer's motivation to participate in the
campaign and his or her usage behavior. The second group of
factors includes sociometric indicators of consumer character -
istics, thus providing information about the type of relationship
that the consumer has with his or her contacts and his or her
resulting social network.
Psychographic Indicators of Consumer Characteristics
As mentioned in the related literature section, according to
Okazaki (2008), in viral marketing campaigns, purposive value
and entertainment value are the primary value dimensions for
consumers. This insight is based on the finding that consumers
gain two types of benefits from participating in sales
promotions:
hedonic and utilitarian benefits (Chandon, Wansink, and
Laurent
2000). Hedonic benefits are primarily intrinsic and can be
associated with entertainment value. Consumers participate
voluntarily and derive value from the fun of interacting with
peers by forwarding a referral (e.g., an ad might be of interest
to
peer recipients) (Dholakia, Bagozzi, and Pearo 2004). A
previous
study found that the entertainment factor influences intended
use
in mobile campaigns (Palka, Pousttchi, and Wiedemann 2009).
Okazaki (2008) found that in a mobile viral marketing
campaign,
the entertainment value directly influences the recipient's
attitude
toward the campaign, which, in turn, influences the recipient's
intention to participate in a mobile viral campaign. Phelps et al.

(2004) showed that the entertainment value is a factor that
increases consumers' forwarding behavior in viral marketing
campaigns conducted via e-mail. Thus, we may presume that
consumers who place high importance on the entertainment
value
of exchanging messages are more likely to enter the reading and
interest stages than consumers who do not value entertainment
to
the same degree. Additionally, the entertainment value can
also influence the decision to refer (i.e., forwarding) behavior
because a text message that addresses consumers who place
high importance on entertainment value causes the recipient to
think about forwarding the text message and motivates them
to forward the mobile advertisement to friends (i.e., decision
to refer stage).
H1. Consumers who place high importance on the entertainment
value of a message are more likely to a) enter the reading stage,
b) enter the interest stage and c) enter the decision to refer
stage.
As utilitarian benefits are instrumental and functional, they
can be associated with purposive value (Okazaki 2008).
Dholakia, Bagozzi, and Pearo (2004) analyzed the influence
of purposive value in network-based virtual communities and
found that purposive value is a predictor of social identity and
a key motive for an individual to participate in virtual
communities. With respect to the mobile context, previous
research found that purposive value has a direct, significant
influence on a consumer's attitude toward a mobile viral
marketing
campaign and that this attitude significantly influences the
intention to participate in mobile marketing campaigns (Okazaki
2008). For some consumers, forwarding a (mobile)
advertisement
in a viral marketing campaign can have a personal and a social

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