Introduction and Annotated Bibliography

A brief introduction describing the specific topic you selected to investigate (within the broad context of viral advertising). Make sure you take into account the comments/suggestions and feedback you received in Assignment I. This is your chance to improve and refine it.

Select three (3) sources from the bibliographic list and write annotation for all three according to the Guide to Annotated Bibliography

Each annotated bibliography should be under 120-words

Write your annotation in paragraph format
Provide a summary of the scope, main points, and central theme of the article
Describe any conclusions that can be drawn from the article
Comment on the intended audience
Compare or contrast this source with another you have cited
Point out any notable biases or gaps you detect
Evaluate and explain why this source is relevant or suitable for your topic

Length and format of paper:

A Word doc, double-spaced 12 pts.

The introduction of your selected topic for research should be under one (1) page long.

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 plac

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 Foundat

ion, In

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: pescher@bwl.lmu.de (C. Pescher),

p.reichhart@bwl.lmu.de (P. Reichhart), spann@spann.de (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

mailto:pescher@bwl.lmu.de

mailto:p.reichhart@bwl.lmu.de

mailto:spann@spann.de

http://dx.doi.org/10.1016/j.intmar.2013.08.001

http://crossmark.crossref.org/dialog/?doi=10.1016%2Fj.intmar.2013.08.001&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 forward
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
meaning (e.g., doing something good for friends by forwarding the
ad). Thus, we hypothesize that consumers who place high
importance on the purposive value of exchanging messages will
display a greater likelihood to enter the reading and interest stages.
We also hypothesize that consumers who place high importance on
the purposive value of a message are more likely to make the
decision to forward the message.

H2. Consumers who place high importance on the purposive
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.

The intensity of usage (e.g., a high quantity of written text
messages) positively influences the probability of trial and
adoption (Steenkamp and Gielens 2003). Thus, consumers with
high usage intensities are more likely to actively participate in a
mobile viral marketing campaign. As mobile viral marketing
campaigns are a fairly new form of advertising, consumers with
high usage intensities are more likely to participate in mobile
viral marketing campaigns and are more likely to forward
messages than consumers with low usage intensities. Therefore,
we propose that usage intensity has an effect on the decision to
forward a mobile advertising text message. The likelihood of
deciding to forward the mobile advertisement increases with
the usage intensity of mobile text messages. This proposition is
consistent with Neslin, Henderson, and Quelch (1985), who
found that the promotional acceleration effect is stronger for
heavy users than it is for other consumers. Godes and Mayzlin
(2009) analyzed the effectiveness of referral activities and
argued that the sales impact from less loyal customers is
greater, but they also highlighted that this greater sales impact
does not mean that the overall referrals by less loyal customers
have a greater impact than those by highly loyal customers.
They concluded that companies who want to implement an
exogenous referral program to drive sales should focus on both
less loyal and highly loyal customers because focusing only on
highly loyal or less loyal customers is not necessarily the
cornerstone of a successful viral marketing campaign. In the
online context, a previous study found that experience with the
Internet influences channel usage behavior (Frambach, Roest,
and Krishnan 2007). Thus, as consumers with high usage
intensity are used to communicating with mobile phones, they
know how to write, read and forward mobile text messages.
Accordingly, it is likely that the threshold to forward a text
message is lower for consumers with high usage intensity than
it is for other consumers and that such consumers are thus more
inclined to refer. Further, the minimal effort required to directly
forward a mobile text message via a cell phone increases the
decision to refer. Thus, we hypothesize that heavy mobile users
will be more likely to refer than will light users.

H3. The usage intensity of the referral medium has a positive
influence on the likelihood of making the decision to refer.

47C. Pescher et al. / Journal of Interactive Marketing 28 (2014) 43–54

Sociometric Indicators of Consumer Characteristics

Sociometric indicators describe the interaction structure of
an individual consumer with his or her surroundings. When
consumers receive an interesting mobile advertising message, it
is likely that they want to find out more about it. Once the
consumer has visited the product homepage, he or she then
considers not only whether the message is worth forwarding but
also to whom it should be forwarded.

Sociometric indicators provide information about the social
network of each individual consumer. This individual network
influences the likelihood of knowing someone who may be
interested in the offered product. Thus, social networks have a
significant impact on the decision-making process in a viral
marketing campaign. The decision to forward the mobile
advertising message depends on two factors: the quality and the
quantity of relations, i.e., the tie strength and the degree centrality.

Tie strength is an important factor in viral marketing and
increases with the amount of time spent with the potential
recipient and with the degree of emotional intensity between
the sender and the potential recipient (Marsden and Campbell
1984). Consumers perceive strong ties to be more influential
than weak ties (Brown and Reingen 1987) because the strong
ties seem more trustworthy (Rogers 1995). Therefore, because
consumers are more motivated to provide high-value informa-
tion to strong ties (Frenzen and Nakamoto 1993), tie strength is
an indicator of the quality of the relationship.

Reagans and McEvily (2003) studied how social network
factors influence knowledge transfer at an R&D firm. To
measure the tie strength, they used two items that are analogous
to those that we used (Burt 1984). Their results indicated that
tie strength positively influences the ease of knowledge
transfer. Thus, network ties increase a person’s capability to
send complex ideas to heterogeneous persons. Overall, they
highlighted the importance of tie strength with respect to the
knowledge transfer process, and they postulate that tie strength
holds a privileged position. Other studies found that weak ties
make non-redundant information available (Levin and Cross
2004). In an online setting, participants were more likely to
share information with strong ties than with weak ties (Norman
and Russell 2006). With respect to viral marketing conducted
via e-mail, previous research has found that tie strength has a
significant influence on whether the recipient examines an
e-mail message sent from a friend (i.e., opens and reads the
message) (De Bruyn and Lilien 2008). Tie strength was also
determined to be less relevant in an online setting compared to an
offline setting (Brown, Broderick, and Lee 2007). In a non-mobile
or non-online context, stronger ties are more likely to activate the
referral flow (Reingen and Kernan 1986). Furthermore, tie
strength is positively related to the amount of time spent receiving
positive referrals (van Hoye and Lievens 1994).

As previously mentioned, research on word-of-mouth behav-
ior has shown that people engage in word-of-mouth for reasons
such as altruism (Sundaram, Mitra, and Webster 1998). However,
Sundaram, Mitra, and Webster (1998) did not control for the
quality of a relationship between sender and recipient. Research
concerning referral reward programs has identified that offering a

reward increases the referral intensity and has a particular impact
on weak ties (Ryu and Feick 2007). Brown and Reingen 1987
found that while strongly tied individuals exchange more
information and communicate more frequently, weak ties play
an important bridging role. Additionally, Granovetter (1973)
stated that one is significantly more likely to be a bridge in the
case of weak ties than in strong ties. In job search, when using
personal networks, it was found that weak ties have a higher rate
of effectiveness when addressing specialists for jobs compared to
strong ties (Bian 1997) and that the income of people using weak
ties was greater than those who used strong ties (Lin, Ensel, and
Vaughn 1981). At the information level, consumers who are
connected via strong ties tend to share the same information
that is rarely new to them, while consumers obtain important
information from weak ties who tend to possess information that
is “new” to them (Granovetter 1973). Consistent with this
finding, Levin and Cross (2004) found that novel insights and
new information are more likely to pass along weak ties than
between strong ties. As in our study, viral marketing information
can be perceived as a novel insight or new information. Given
that consumers are more likely to send a message to someone if
the content is new to the receiver, it is more likely that consumers
will forward the text message to a weak tie than to a strong tie.
Thus, we presume that consumers prefer to forward mobile
advertising text messages, such as the one used in our study (for a
new music CD), to other customers with whom they are connected
through weak ties.

Similarly, Frenzen and Nakamoto (1993) postulate that the
motivation to share information or refer a product is driven by the
value of the information and the cost of sharing. They identified
(though only for weak ties) an influence of word-of-mouth that is
spread by value and opportunity costs. In our case, when customers
forward a mobile advertising message, the opportunity cost is low
because forwarding can be done easily and without any effort (e.g.,
compared to meeting the friend personally in the city). In the case
of strong ties, the preferences of the recipient (e.g., for products) are
well known to the sender, whereas these preferences are unclear for
weak ties. Thus, people who want to do something beneficial for
their contacts know the likelihood that it will benefit the recipient in
the case of strong ties, but they do not know the benefit it may bring
to weak ties. In our study, this benefit involved sharing information
to acquire a free new music CD. Because the costs of sharing are
low using cell phones, people are more inclined to forward such
information. With respect to strong ties, people know whether the
information is relevant. Furthermore, relationships to strong ties are
more important than relationships to weak ties. Importantly,
people do not want to displease strong ties by sending irrelevant
information or cause information overload with unsuitable
information. In the case of receiving annoying information, the
recipient could ask the sender not to forward text messages
anymore. This sanction is more painful when received from strong
ties (e.g., good friends) than from weak ties (e.g., acquaintances)
because the weak ties are less important to the sender. This is
similar to the finding that people who are dissatisfied (e.g., with a
product or service) are more likely to advise against the purchasing
of the product to strong ties rather than to weak ties (Wirtz and
Chew 2002), which may also be due to the sanction issue. In

48 C. Pescher et al. / Journal of Interactive Marketing 28 (2014) 43–54

mobile viral marketing campaigns, because the sharing of
information is easy and not costly, factors such as knowledge
about the preferences of the recipient or the fear of annoying
strong ties become more important when deciding whether and to
whom to refer the message.

Thus, we hypothesize the following:

H4. Tie strength has a negative influence on the likelihood of
making the decision to refer.

Diffusion occurs via replication or transfer, e.g., of used goods
(Borgatti 2005). Within the group of replication processes,
replications can occur one at a time (serial duplication),
e.g., gossip or viral infections, or simultaneously (parallel
duplication), e.g., e-mail broadcasts or text messages on mobile
phones. Degree centrality is a measure that is suitable for
analyzing processes in which a message is duplicated simulta-
neously because it can be interpreted as a measure of immediate
influence — the ability to “infect” others directly (Borgatti 2005,
p 62). Hubs are identified using degree centrality because they are
actors who possess a high number of direct contacts (Goldenberg
et al. 2009) and because they know a high number of people to
whom they can forward a message and can thus influence more
people (Hinz et al. 2011). Hubs also adopt earlier in the diffusion
process. In detail, innovative hubs increase the speed of the
adoption process, while follower hubs influence the size of the
total market (Goldenberg et al. 2009). Further, hubs tend to be
opinion leaders (Kratzer and Lettl 2009; Rogers and Cartano
1962) because they have a high status and serve as reference
points in the information diffusion process. Small groups of
opinion leaders often initiate the diffusion process of innovations
(Van den Bulte and Joshi 2007). Czepiel (1974) analyzed
whether centrality in opinion networks influenced the adoption
and found no significance for this, a finding that is contrary to
other literature in the field (e.g., Goldenberg et al. 2009).
Furthermore, targeting central customers leads to a significant
increase in the spread of marketing messages (Kiss and Bichler
2008). In a viral marketing campaign for a mobile service where
referrals are conducted via the Internet (i.e., online referral
system) and not via forwarding a mobile text message, the results
showed that high centrality increases the likelihood of participa-
tion (Hinz et al. 2011). Therefore, we hypothesize that degree
centrality has a positive influence on the decision to refer.

H5. Degree centrality has a positive influence on the likelihood
of making the decision to refer.

Empirical Study

Goal and Research Design

The goal of this empirical study is to test the hypotheses
derived above using a three-stage model that represents the
stages of a consumer’s decision-making process in a mobile
referral context.

In our field study, we conducted a mobile marketing
campaign. The randomly chosen participants had previously
agreed to receive mobile advertising text messages on their cell

phones (opt-in program). We sent a text message to 26,137
randomly chosen customers that included a link to a website
and the notice that they could download a recently released
music CD for free. The only purpose of this website was to give
the campaign’s participants the option to download the music
CD for free. In the text message, they were also asked to
forward the message to their contacts. The exact text of the
message stated, “Amazing! You & your friends will receive a
new CD as an MP3 for free! No subscription! Available online:
URL.com -N Forward this text message to your friends now!”

One week later, we sent another text message to all of the
participants who received the initial mobile advertising text
message with a link to an online survey. This second text
message contained the request to participate in an online
survey. We provided one 100 Euro and two 50 Euro prizes as
incentives to participate in the survey. The winners were drawn in a
lottery. In the questionnaire, the participants provided information
about their behavior during different stages of the referral process
and about their psychological constructs and egocentric networks
(Burt 1984). Egocentric networks are networks that analyze the
focal actor and the actor’s direct friends as well as the relations that
exist between them (Burt 1984).

Measures

The psychographic constructs “purposive value” and “enter-
tainment value” were adapted from Dholakia, Bagozzi, and Pearo
(2004) and Okazaki (2008). Concerning the operationalization of
both psychographic constructs, i.e., purposive value and entertain-
ment value, we addressed the consumers’ characteristics to identify
the consumers who place high importance on the purposive value
of messages in general. We analyzed the consumer’s characteristics
concerning both constructs, i.e., the importance of the purposive
value and the entertainment value for respondents. Items were
measured on a seven-point Likert scale, using scale points from
“do not agree at all” (1) to “totally agree” (7). We operationalized
the “usage intensity” by surveying the number of text messages
each participant wrote per day (see Appendix A for details).

To measure consumers’ social networks, we surveyed their
egocentric networks (Burt 1984; Fischer 1982; McCallister and
Fischer 1978; Straits 2000). Egocentric networks are defined as
the direct relationships between an individual consumer (or
ego) and other consumers (or alters) and the relationships that
exist between the alters. These are small networks of one focal
actor called the “ego”, the participant in the survey, and his or
her contacts, called “alters”. The difference between a regular
network and an egocentric network is that in the latter, all of the
necessary information is obtained from one actor, which makes
it a feasible method for obtaining samples that are representa-
tive of a large-scale population. To access the respondents’ core
networks, we used the term “generator,” which was taken from
Burt (1984), and adapted it to the specific characteristics of this
study (see Appendix A for details). The participants first generated
their list of alters by identifying their most frequent contacts and
were then asked a series of questions, which helped us to gain
additional insights in their social network, including the strength
of the relationship with each contact. Because each egocentric

49C. Pescher et al. / Journal of Interactive Marketing 28 (2014) 43–54

network is calculated based on information from a single
respondent, such networks are usually treated as undirected.
Based on this information, we calculated the degree centrality,
which is the number of ties for a node, and average tie strength
for each network. Marsden (2002) showed that the egocentric
centrality measures are generally good proxies for sociometric
centrality measures.

Model

The model consists of a funnel of three successive decisions:
(i) reading the message, (ii) visiting the homepage (interest) and
(iii) forwarding the message (decision to refer). In each stage,
the number of observations diminishes because only consumers
who took action in the last stage can take another action in the
next stage, i.e., only consumers who read the message can access
the homepage. Fig. 1 shows that the model is hierarchical, nested,
and sequential, which leads to desirable statistical properties.
Therefore, we use Maddala’s (1983, p 49) sequential response
model, which is also known as a model for nested dichotomies
(Fox 1997). We follow the argumentation of De Bruyn and Lilien
(2008), who adapted a model of Maddala (1983, pp 49) to the
context of consumer decision-making (see Appendix B for our
model). We fit the model by simultaneously maximizing the
likelihood functions of the three dichotomous models in Stata 12.
Each likelihood function incorporates the estimated probabilities
of the preceding stages.

De Bruyn and Lilien (2008) argued that consumers do not
drop out at random in this process because it is a process of
self-selection, which may raise statistical concerns. However,
the parameter estimates for the structure of the model used in
this paper have been shown to be unaffected by changes in the
marginal distributions of the variables (Bishop, Fienberg, and
Holland 1975; Mare 1980).

Consumer

does not read

message
n=309

Consumer
does not visit

homepage
n=194

Consumer
does not for-

ward messag
n=296

Consumer
receives
message

n=943

Consumer
reads

message
n=634

Fig. 1. Consumers’ decision-making

Results

Descriptive Statistics and Bias Tests

In all, 943 subjects responded to the survey. Of these, 634 read
the initial message (reading), 440 visited the homepage (interest)
and 144 consumers forwarded the message (decision to refer).
Table 1 shows the correlations and descriptive statistics among
the variables in this study.

First, we compare the demographic characteristics of the
survey respondents with the demographic characteristics of the
entire customer sample. Of the 943 survey respondents, 28%
are female, which is in line with the entire sample. In addition
we observed the age distribution of the survey respondents as
well as the entire customer sample and found that the groups
are essentially consistent (see Table 2).

Second, we compared consumers who read the text message
(Nread = 634) with those who did not read the initial text message
(Nnoread = 309) with respect to their surveyed demographics and
cell phone usage behavior. We found no significant difference
between the groups for demographics (female: Mread = 27.0%,
Mnonread = 30.1%, p N .31; age: Mread = 29.2 yrs, Mnonread =
30.6 yrs, p N .06), monthly cell phone usage (Mread = 29.28 €,
Mnonread = 29.62 €, p N .95) or age and gender.

Three-stage Decision-making Model

We include all explanatory variables in the estimation of
every stage to avoid an omitted variable bias. Table 3 shows the
results of our sample selection model.

Entertainment Value and Purposive Value (H1 and H2)
We find significant influence concerning consumers who place

high importance on the entertainment value of exchanging mobile

Consumer
forwards
message

n=144

U
N

A
W

E
R

E
A

D
IN

S
TA

G
E

E
R

E
S

T
S
TA
G
E

D
E

C
IS

IO
N

T
O

R
E

F
E

R
S
TA
G
E

Consumer
visits

homepage
n=440

process in the viral campaign.

Table 1
Descriptive statistics.

Mean StD 1 2 3 4 5

1. Entertainment value 4.19 1.53
2. Purposive value 3.81 2.01 .46⁎⁎

3. Usage intensity 7.53 19.73 .14⁎⁎ .08 ⁎⁎

4. Tie strength 3.10 .80 .11⁎⁎ .05 .01
5. Degree centrality 3.04 1.46 .08⁎⁎ .03 .05 .02
6. Age 29.69 11.08 −.10⁎⁎ .05 −.14⁎⁎ .02 −.17⁎⁎

Notes: means, standard deviations and correlations, N = 943.
⁎⁎ p b .05.

Table 3
Results of the three-stage model.

Stage: Reading Stage: Interest Stage: Decision
to refer

Coefficient SE Coefficient SE Coefficient SE

Entertainment value .116 ⁎⁎ .054 .181 ⁎⁎ .070 .204 ⁎⁎ .101
Purposive value .062 .045 .167 ⁎⁎ .063 .600 ⁎⁎ .095
Usage Intensity −.003 .003 .008 .008 .010 ⁎ .006
Tie strength −.219 ⁎⁎ .094 −.156 .114 −.461 ⁎⁎ .153
Degree centrality −.021 .050 −.010 .064 .114 .085
Age −.012 ⁎ .006 −.014 .009 .036 ⁎⁎ .012
Gender a .098 .160 .580 ⁎⁎ .200 −.229 .283
Intercept 1.069 ⁎⁎ .461 −.100 .570 −4.264 ⁎⁎ .814
n 943
Log likelihood −1174.81
Wald chi2 180.58
Prob N chi2 b.01

a Dummy coding (0 = female, 1 = male).
⁎ p b .10.
⁎⁎ p b .05.

50 C. Pescher et al. / Journal of Interactive Marketing 28 (2014) 43–54

text messages with regard to the reading stage (coefficient:
.116/H1a), the interest stage (coefficient: .181/H1b) and the
decision to refer stage (coefficient: .204/H1c). Furthermore,
we find a significant increase in the likelihood of entering the
interest stage (coefficient: .167/H2b) as well as the decision to
refer stage (coefficient: .600/H2c) for consumers who place
high importance on purposive value. However, no support can
be found for H2a.

Overall, consumers who place higher importance on the
entertainment value of exchanging text messages and who
place higher importance on the purposive value demonstrate an
increased likelihood of entering the interest and decision to
refer stages.

Usage Intensity (H3)
Our results show that the influence of usage intensity on the

decision to refer a mobile text message is only significant at the
10% level (coefficient .010/H3). Thus, consumers who are used
to writing mobile text messages are more likely to forward
mobile text messages as well as the advertising text message.
This result is consistent with previous findings that usage
intensity is positively related to user behavior (Gatignon and
Robertson 1991).

Tie Strength (H4)
Our results support H4: Tie strength significantly decreases

the likelihood that consumers decide to send and forward a
mobile text message (coefficient: −.461/H4). Thus, lower levels
of tie strength increase the likelihood of the decision to send
advertising text messages. A previous study found that the

Table 2
Demographics of survey respondents and the entire customer sample.

Percentage

Survey respondents Entire customer sample

Age b20 20% 11%
20–29 37% 36%
30–39 22% 28%
40–49 15% 17%
50–59 4% 6%
≥60 1% 2%

receivers of unsolicited e-mails tend to pay more attention to
messages from close contacts (i.e., high-quality contacts) (De
Bruyn and Lilien 2008). However, their study focused on
consumers who are actively searching for relevant information
and are thus receivers of information. In contrast, the present
study addresses mobile advertising text messages that are sent
from a consumer to the consumer’s contacts without being
solicited. In other words, we focus on the sender of the message.
The difference between the sender and the receiver of a message
provides a solid explanation for the differences in the results
between the two studies. Further, we find a negative influence of
tie strength on the likelihood to read the message, which may be
explained by the low tendency of consumers with strong tie
relationships to read firm-initiated text messages.

To gain deeper insight into the negative impact of tie
strength on the decision to forward a message, we conduct an
additional analysis and find that the average tie strength
between senders and receivers of forwarded messages is 2.99.
This tie strength is significantly less than the average tie
strength of 3.09 for connections where no text messages were
forwarded (p b .05).

Degree Centrality (H5)
In testing H5, we focus on the influence of degree centrality

on the decision to refer and find that the number of contacts
(i.e., degree centrality) has no influence on the decision to refer.
Thus, H5 is not supported.

In addition to testing the hypotheses, we examine the
influence of gender on each stage of the decision-making
process. The results show that gender has a significant influence
only on the interest stage (coefficient: .580). No significant
influence of gender is identified for the reading or decision to
refer stages. Further, we test the effect of age in the
decision-making process and find that age has a significant
positive influence on the decision to refer stage (coefficient:
.036).

51C. Pescher et al. / Journal of Interactive Marketing 28 (2014) 43–54

Discussion

This paper develops a three-stage model to analyze the
decision-making process of consumers in mobile viral marketing
campaigns. This is an important area to study because companies
actively approach only a small number of consumers in viral
marketing campaigns. Therefore, additional information about
these consumers, their decision-making processes and the factors
that influence the consumers may help determine the success or
failure of viral marketing campaigns. Only a few extant studies
focus on mobile viral marketing campaigns. Due to the increased
use and penetration rate of cell phones and smartphones, most
consumers can be addressed via this new medium and channel.
Furthermore, cell phones combine unique characteristics such as
ubiquitous computing, always on and immediate reactions, thus
making mobile phones an attractive marketing channel for viral
marketing campaigns.

The three-stage model approach allows us to gain a deeper
understanding of consumers’ decision-making processes in
mobile viral marketing campaigns. This study has produced
several key findings.

The first key finding is the important role of consumers
who place high importance on the purposive value and
entertainment value of a message during the decision-making
process. We find that the entertainment value significantly
influences all three stages. Purposive value significantly
influences the interest stage and the decision to refer stage,
but it does not influence the reading stage. This finding is
relevant when conducting a mobile viral marketing campaign.
To attract customer attention, and thus to successfully pass the
reading stage, companies should develop campaigns that
entertain customers. To have a significant influence on the
decision to refer, it is important to address consumers who
place importance on both purposive and entertainment value.
However, Dholakia, Bagozzi, and Pearo (2004) found that in
online communities, purposive value has no direct effect on
participation behavior. The differences between their results and
our results can potentially be attributed to the differences between
online technology and cell phones. We suggest that purposive
value is mobile-specific because the mobile setting differs from
the Internet. People tend to spend a considerable amount of
time in online communities and therefore have potentially large
amounts of content to read. This makes it difficult to identify the
content that may be meaningful to the contacts. On the contrary, at
least to date, customers receive a limited amount of mobile text
messages. Thus, the purposive value can be judged, and if
customers believe that the text message is useful and has purpose,
they will be interested in it and will eventually decide to forward it.

We find that tie strength plays an important role in the last
stage of the decision-making process and has a negative
influence on both the reading stage and the decision to refer
stage. In other words, consumers are more likely to pass
messages on to weak ties (i.e., low-quality contacts). Further,
usage intensity has a positive influence at the 10-percent level,
which indicates that heavy users may be influential in mobile
viral marketing campaigns because they have a lower threshold
to pass information to their contacts. Thus, heavy users are

more influential because of their communication habits. An
interesting finding is that degree centrality has no influence on
the decision-making process.

Our results indicate that one reason for the failure of mobile
viral marketing campaigns is that consumers tend to forward
messages to consumers on whom they have limited impact.
Consumers with low-quality contacts (i.e., weak ties) are more
likely to forward the message because the perceived risk,
which can include social sanctions, of forwarding a message is
low. Accordingly, the results of this study indicate a behavior
that might limit the impact of viral campaigns. Specifically,
customers who predominantly possess weak ties are more
likely to read the message, and they are more likely to pass it
on to other customers with predominantly weak ties. In
contrast, the receivers of the message tend to make their
purchase decisions based on the content that they receive from
high-quality (i.e., strong tie) contacts because such ties are
perceived to be more trustworthy when making a purchase
decision (Rogers 1995). Thus, the viral process may lead to a
high number of referrals that are ignored by their receivers,
thus potentially limiting the success of the campaign. Future
research should therefore examine the circumstances under
which consumers make their purchase decisions and the products
that they choose based on recommendations from strong and
weak ties.

This study has several limitations that open additional
avenues for future research. First, because of our setting and
because we conducted a viral marketing campaign in a mobile
environment via text messages, we were not able to directly
observe the referrals made. In our study, this variable is
self-reported via a survey, which is a limitation of the study.
Further research should conduct an experimental setting where
it is possible to observe the forwarding behavior using
behavioral data rather than survey data. Second, the partici-
pants of this study were members of an opt-in program
and had already agreed to receive mobile advertising text
messages for advertising reasons. Therefore, it remains
unclear how consumers who have not consented to receiving
messages would react to unsolicited mobile advertising
messages. Third, mobile marketing is still an emerging field
in comparison to e-mail marketing. Therefore, it is likely that
consumers will pay attention to these (mobile marketing)
campaigns because they are rather novel. It remains to be seen
how these results may change with the increasing prevalence
of mobile marketing campaigns in the future. Fourth, we
studied only one product category, a new music CD. Future
research should analyze what products or services are (more or
less) suitable for mobile viral marketing campaigns. Fifth, in
this study, we only focused on data from the responses of
consumers who were seeded by the company. To further
generalize the results, future research could also analyze
consumers who receive a message from a friend. Finally, we
only analyzed mobile viral marketing via text messages. Future
studies could analyze and compare the findings with mobile
viral marketing campaigns using media-rich formats such as
multimedia messages because these formats can offer more
entertaining content to recipients.

52 C. Pescher et al. / Journal of Interactive Marketing 28 (2014) 43–54

Appendix A. Measurement Scales

Entertainment value
(Cronbach’s α = .804)

I want to entertain my friends by sending enjoyable
information.
Information exchange is fun by itself.
It is a good way to spend time in an enjoyable way.

Purposive value
(Cronbach’s α = .738)

I feel like spreading information I have discovered
to my friends.
I want to send information to my friends who may
be interested in it.

Usage intensity Number of mobile text messages each survey
participant wrote per day.

Network items
(Burt 1984)

Degree centrality (=number of contacts the respondent
names): Looking back over the last six months, who are
the people with whom you discussed an important
personal matter? Please write their first names or initials
in the boxes below.
Tie strength: How close do you feel to these people?

Note: Items for entertainment value & purposive value on seven-point Likert

Appendix B. Sequential Logit Model Specification

Consider the following model (cf. De Bruyn and Lilien 2008):

Y = 1 if the recipient has not read the text message.
Y = 2 if the recipient has read the text message but has not

visited the website.
Y = 3 if the recipient has visited the website but has not

forwarded the message.
Y = 4 if the recipient has forwarded the message.

The probabilities can be written as follows (Amemiya 1975):

P1(Y = 1) = F(β1′x)
P2(Y = 2) = [1 − F(β1′x)]F(β2′x)
P3(Y = 3) = [1 − F(β1′x)][1 − F(β2′x)]F(β3′x)
P4(Y = 4) = [1 − F(β1′x)][1 − F(β2′x)][1 − F(β3′x)].

The parameters β1 are estimated for the entire sample by
dividing the sample into those who read the text message and those
who did not. The parameters β2 are estimated from the subsample
of recipients who read the text message by dividing it into two
groups: those who visited the website and those who did not. The
parameters β3 are estimated from the subsample of recipients who
visited the website by dividing the subsample into two groups:
those who forwarded the message and those who did not.

The likelihood functions for the above sequential logit model
can be maximized by sequentially maximizing the likelihood
functions of the three dichotomous models (De Bruyn and Lilien
2008; Maddala 1983).

References

Amemiya, Takeshi (1975), “Qualitative Response Models,” Annals of
Economic and Social Measurement, 4, 3, 363–72.

Bacile, Todd J., Christine Ye, and Esther Swilley (2014), “Consumer Co-
production of Personal Media Marketing Communication: The Case of
Mobile Coupons,” Journal of Interactive Marketing (forthcoming).

scale. Tie strength measured on five-point Likert scale.

Bampo, Mauro, Michael T. Ewing, Dineli R. Mather, David Stewart, and Mark
Wallace (2008), “The Effects of the Social Structure of Digital Networks on
Viral Marketing Performance,” Information Systems Research, 19, 3,
273–90.

Bettman, James R. (1979), An Information Processing Theory of Consumer
Choice. Reading, MA: Addison-Wesley.

Bian, Yanjie (1997), “Bringing Strong Ties Back In: Indirect Ties, Network
Bridges, and Job Searches in China,” American Sociological Review, 62, 3,
366–85.

Bishop, Yvonne M.M., Stephen E. Fienberg, and Paul W. Holland (1975),
Discrete Multivariate Analysis. Cambridge, MA: MIT Press.

Bone, Paula Fitzgerald (1995), “Word-of-Mouth Effects on Short-term and
Long-term Product Judgments,” Journal of Business Research, 32, 3,
213–23.

Borgatti, Stephen P. (2005), “Centrality and Network Flow,” Social Networks,
27, 55–71.

Bowman, Douglas and Das Narayandas (2001), “Managing Customer-initiated
Contacts with Manufacturers: The Impact on Share of Category Require-
ments and Word-of-Mouth Behavior,” Journal of Marketing Research, 38,
3, 281–97.

Brown, Jacqueline Johnson and Peter H. Reingen (1987), “Social Ties and
Word-of-Mouth Referral Behavior,” Journal of Consumer Research, 14, 3,
350–62.

Brown, Jo, Amanda J. Broderick, and Nick Lee (2007), “Word of Mouth
Communication Within Online Communities: Conceptualizing the Online
Social Network,” Journal of Interactive Marketing, 21, 3, 2–20.

Burt, Ronald S. (1984), “Network Items and the General Social Survey,” Social
Networks, 6, 4, 293–339.

Chandon, Pierre, Brian Wansink, and Gilles Laurent (2000), “A Benefit
Congruency Framework of Sales Promotion Effectiveness,” The Journal of
Marketing, 64, 4, 65–81.

Czepiel, John A. (1974), “Word of Mouth Processes in the Diffusion of a
Major Technological Innovation,” Journal of Marketing Research, 11, 2,
172–80.

De Angelis, Matteo, Andrea Bonezzi, Alessandro M. Peluso, Derek D. Rucker,
and Michele Costabile (2012), “On Braggarts and Gossips: A Self-
Enhancement Account of Word-of-Mouth Generation and Transmission,”
Journal of Marketing Research, 49, 4, 551–63.

De Bruyn, Arnaud and Gary Lilien (2008), “A Multi-stage Model of Word-of-
Mouth Influence Through Viral Marketing,” International Journal of
Research in Marketing, 25, 3, 151–63.

De Matos, Celso A. and Carlos A.V. Rossi, (2008), “Word-of-Mouth
Communications in Marketing: A Meta-analytic Review of the Antecedents
and Moderators,” Journal of the Academy of Marketing Science, 36, 4,
578–96.

Dholakia, Utpal M., Richard P. Bagozzi, and Lisa Pearo (2004), “A Social
Influence Model of Consumer Participation in Network- and Small-group-
based Virtual Communities,” International Journal of Research in Marketing,
21, 3, 241–63.

Dickinger, Astrid and Mirella Kleijnen (2008), “Coupons Going Wireless:
Determinants of Consumer Intentions to Redeem Mobile Coupons,”
Journal of Interactive Marketing, 22, 3, 23–39.

Drossos, Dimitris, George M. Giaglis, George Lekakos, Flora Kokkinaki,
and Maria G. Stavraki (2007), “Determinants of Effective SMS
Advertising: An Experimental Study,” Journal of Interactive Marketing,
7, 2, 16–27.

East, Robert, Kathy Hammond, and Wendy Lomax (2008), “Measuring the Impact
of Positive and Negative Word of Mouth on Brand Purchase Probability,”
International Journal of Research in Marketing, 25, 3, 215–24.

EITO (Feb. 2012), “More Than Five Billion Mobile Phone Users,” http://www.
eito.com/press/Press-Releases-2010/More-than-five-billion-mobile-phone-users
Accessed 13. Feb. 2012.

Fischer, Claude S. (1982), To Dwell Among Friends — Personal Networks in
Town and City. Chicago: The University of Chicago Press.

Fox, John (1997), Applied Regression Analysis, Linear Models, and Related
Methods. Thousand Oaks: Sage.

Frambach, Ruud T., Henk C. Roest, and Trichy V. Krishnan (2007), “The
Impact of Consumer Internet Experience on Channel Preference and Usage

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0005

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0375

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0010

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0015

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0020

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0025

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0030

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0035

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0040

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0165

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0045

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0050

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0055

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0060

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0065

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0070

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0075

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0080

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0085

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0090

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0095

http://www.eito.com/press/Press-Releases-2010/More-than-five-billion-mobile-phone-users

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0105

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0110

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0115

53C. Pescher et al. / Journal of Interactive Marketing 28 (2014) 43–54

Intentions Across the Different Stages of the Buying Process,” Journal of
Interactive Marketing, 21, 2, 26–41.

Frenzen, Jonathan and Kent Nakamoto (1993), “Structure, Cooperation, and the
Flow of Market Information,” The Journal of Consumer Research, 20, 30,
360–75.

Gatignon, Hubert and Thomas S. Robertson (1991), “Innovative Decision
Processes,” in Handbook of Consumer Behavior,” Thomas S. Trobertson
and Harold H. Kassarjian, editors. NJ: Prentice-Hall: Englewood Cliffs.
p. 316–48.

Godes, David and Dina Mayzlin (2005), “Using Online Conversations to Study
Word-of-Mouth Communication,” Marketing Science, 23, 4, 545–60.

——— and Dina Mayzlin (2009), “Firm-created Word-of-Mouth Commu-
nication: Evidence From a Field Test,” Marketing Science, 28, 4,
721–39.

Goldenberg, Jacob, Sangman Han, Donald R. Lehmann, and Jae Weon Hong
(2009), “The Role of Hubs in the Adoption Process,” Journal of Marketing,
73, 3, 1–13.

Granovetter, Mark (1973), “The Strength of Weak Ties,” American Journal of
Sociology, 78, 6, 1360–80.

Hennig-Thurau, Thorsten, Kevin P. Gwinner, Gianfranco Walsh, and Dwayne D.
Gremler (2004), “Electronic Word-of-Mouth via Consumer-opinion Plat-
forms: What Motivates Consumers to Articulate Themselves on the Internet?,”
Journal of Interactive Marketing, 18, 1, 38–52.

Herr, Paul M., Frank R. Kardes, and John Kim (1991), “Effects of Word-of-
Mouth and Product-attribute Information on Persuasion: An Accessibility-
Diagnosticity Perspective,” Journal of Consumer Research, 17, 4,
454–62.

Hinz, Oliver, Bernd Skiera, Christian Barrot, and Jan Becker (2011), “Seeding
Strategies for Viral Marketing: An Empirical Comparison,” Journal of
Marketing, 75, 6, 55–71.

Iyengar, Raghuram, Christophe Van den Bulte, and Thomas W. Valente (2011),
“Opinion Leadership and Social Contagion in New Product Diffusion,”
Marketing Science, 30, 2, 195–212.

Kiss, Christina and Martin Bichler (2008), “Identification of Influencers —
Measuring Influence in Customer Networks,” Decision Support Systems,
46, 1, 233–53.

Kleijnen, Mirella, Annouk Lievens, Ko de Ruyter, and Martin Wetzels (2009),
“Knowledge Creation Through Mobile Social Networks and its Impact on
Intentions to Use Innovative Mobile Services,” Journal of Service Research,
12, 1, 15–35.

Kratzer, Jan and Christopher Lettl (2009), “Distinctive Roles of Lead Users and
Opinion Leaders in the Social Networks of Schoolchildren,” Journal of
Consumer Research, 36, 4, 646–59.

Krishnamurthy, Sandeep (2001), “Viral Marketing: What Is It and Why Should
Every Service Marketer Care?,” Journal of Services Marketing, 15, 6, 422–4.

Lavidge, Robert J. and Gary A. Steiner (1961), “A Model for Predictive
Measurements of Advertising Effectiveness,” Journal of Marketing, 25, 6,
59–62.

Levin, Daniel Z. and Rob Cross (2004), “The Strength of Weak Ties You Can
Trust: The Mediating Role of Trust in Effective Knowledge Transfer,”
Management Science, 50, 11, 1477–90.

Lin, Nan, Walter M. Ensel, and John C. Vaughn (1981), “Social Resources and
Strength of Ties: Structural Factors in Occupational Status Attainment,”
American Sociological Review, 46, 4, 393–405.

Maddala, G.S. (1983), Limited Dependent and Qualitative Variables in
Econometrics. Cambridge: Cambridge University Press.

Mare, Robert D. (1980), “Social Background and School Continuation Decisions,”
Journal of the American Statistical Association, 75, 370, 295–305.

Marsden, Peter V. and Karen E. Campbell (1984), “Measuring Tie Strength,”
Social Forces, 63, 2, 482–501.

——— (2002), “Egocentric and Sociocentric Measures of Network Central-
ity,” Social Networks, 24, 4, 407–22.

Maxham, James G. and Richard G. Netemeyer (2002), “A Longitudinal Study
of Complaining Customers’ Evaluations of Multiple Service Failures and
Recovery Efforts,” Journal of Marketing, 66, 4, 57–71.

McCallister, Lynne and Claude S. Fischer (1978), “A Procedure for
Surveying Personal Networks,” Sociological Methods & Research, 7, 2,
131–48.

Moldovan, Sarit, Jacob Goldenberg, and Amitava Chattopadhyay (2011),
“The Different Roles of Product Originality and Usefulness in Generating
Word-of-Mouth,” International Journal of Research in Marketing, 28, 2,
109–19.

Neslin, Scott A., C. Henderson, and J. Quelch (1985), “Consumer Promotions
and the Acceleration of Product Purchases,” Marketing Science, 4,
147–65.

Norman, Andrew T. and Cristel A. Russell (2006), “The Pass-along
Effect: Investigating Word-of-Mouth Effects on Online Survey
Procedures,” Journal of Computer-Mediated Communication, 11, 4,
1085–103.

Okazaki, Shintaro (2008), “Determinant Factors of Mobile-based Word-of-
Mouth Campaign Referral Among Japanese Adolescents,” Psychology and
Marketing, 25, 8, 714–31.

Palka, Wolfgang, Key Pousttchi, and Dietmar G. Wiedemann (2009), “Mobile
Word-of-Mouth — A Grounded Theory of Mobile Viral Marketing,”
Journal of Information Technology, 24, 2, 172–85.

Phelps, Joseph E., Regina Lewis, Lynne Mobilio, David Perry, and Niranjan
Raman (2004), “Viral Marketing or Electronic Word-of-Mouth Advertising:
Examining Consumer Responses and Motivations to Pass Along Email,”
Journal of Advertising Research, 44, 4, 333–48.

Porter, Lance and Guy J. Golan (2006), “From Subservient Chickens to Brawny
Men: A Comparison of Viral Advertising to Television Advertising,”
Journal of Interactive Advertising, 6, 2, 26–33.

Portes, A. (1998), “Social Capital: Its Origins and Applications in Modern
Sociology,” Annual Review of Sociology, 24, 1–24.

Reagans, Ray and Bill McEvily (2003), “Network Structure and Knowledge
Transfer: The Effects of Cohesion and Range,” Administrative Science
Quarterly, 48, 2, 240–67.

Reichhart, Philipp, Christian Pescher, and Martin Spann (2013), “A
Comparison of the Effectiveness of E-mail Coupons and Mobile Text
Message Coupons for Digital Products,” Electronic Markets, 23, 3,
217–25.

Reingen, Peter H. and Jerome B. Kernan (1986), “Analysis of Referral
Networks in Marketing: Methods and Illustration,” Journal of Marketing
Research, 23, 4, 370–8.

Rogers, Everett M. and David G. Cartano (1962), “Methods of Measuring
Opinion Leadership,” Public Opinion Quarterly, 26, 3, 435–41.

——— (1995), Diffusion of Innovations. New York: The Free Press.
Ryu, Gangseog and Lawrence Feick (2007), “A Penny for Your Thoughts:

Referral Reward Programs and Referral Likelihood,” Journal of Marketing,
71, 1, 84–94.

Steenkamp, Jan-Benedict E.M. and Katrijn Gielens (2003), “Consumer and
Market Drivers of the Trial Probability of New Consumer Packaged
Goods,” Journal of Consumer Research, 30, 3, 368–84.

Straits, Bruce C. (2000), “Ego’s Important Discussants or Significant People:
An Experiment in Varying the Wording of Personal Network Name
Generators,” Social Networks, 22, 2, 124–40.

Sultan, Fareena, Andrew J. Rohm, and Tao Gao (2009), “Factors
Influencing Consumer Acceptance of Mobile Marketing: A Two-
country Study of Youth Markets,” Journal of Interactive Marketing,
23, 4, 308–20.

Sundaram, D.S., Kaushik Mitra, and Cynthia Webster (1998), “Word-of-Mouth
Communication: A Motivational Analysis,” Advances in Consumer
Research, 25, 527–31.

Trusov, Michael, Randolph E. Bucklin, and Koen Pauwels (2009),
“Effects of Word-of-Mouth Versus Traditional Marketing: Findings
From an Internet Social Networking Site,” Journal of Marketing, 73, 5,
90–102.

Tsang, Melody M., Shu-Chun Ho, and Ting-Peng Liang (2004), “Consumer
Attitudes Toward Mobile Advertising: An Empirical Study,” International
Journal of Electronic Commerce, 8, 3, 65–78.

Van den Bulte, Christophe and Yogesh Joshi (2007), “New Product Diffusion
with Influentials and Imitators,” Marketing Science, 26, 3, 400–21.

Van der Lans, Ralf, Gerrit Van Bruggen, Jehoshua Eliashberg, and
Berend Wierenga (2010), “A Viral Branching Model for Predicting
the Spread of Electronic Word-of-Mouth,” Marketing Science, 29, 2,
348–65.

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0115

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0120

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0260

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0130

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0125

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0135

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0140

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0145

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0150

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0155

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0160

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0170

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0175

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0180

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0185

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0190

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0195

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0200

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0205

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0210

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0220

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0215

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0225

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0230

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0235

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0240

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0245

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0265

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0270

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0275

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0280

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0285

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0290

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0295

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0300

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0310

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0305

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0315

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0320

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0325

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0330

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0335

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0340

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0345

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0350

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0355

54 C. Pescher et al. / Journal of Interactive Marketing 28 (2014) 43–54

van Hoye, Greet and Filip Lievens (1994), “Tapping the Grapevine: A Closer
Look at Word-of-Mouth as a Recruitment Source,” Journal of Applied
Psychology, 94, 2, 341–52.

Wirtz, Jochen and Patricia Chew (2002), “The Effects of Incentives, Deal
Proneness, Satisfaction and Tie Strength on Word-of-Mouth Behaviour,”
International Journal of Service Industry Management, 13, 2, 141–62.

Wojnicki, Andrea C. and David B. Godes (2008), “Word-of-Mouth as Self-
Enhancement,” Working Paper. University of Toronto/Harvard Business
School.

Christian Pescher: Research interests include B2B and B2C e-commerce,
innovation, and social networks in marketing and forecasting.

Philipp Reichhart: Research interests include e- and m-commerce, mobile
marketing, consumer behavior, word of mouth, social network and location-
based services.

Martin Spann is a professor of electronic commerce and digital markets
at the Munich School of Management, Ludwig-Maximilians-University Munich,
Germany. His research interests include e-commerce, mobile marketing,
prediction markets, interactive pricing mechanisms, and social networks. He has
published in Management Science, Marketing Science, Journal of Marketing,
Information Systems Research, MIS Quarterly, Journal of Product Innovation
Management, Journal of Interactive Marketing, Decision Support Systems, and
other journals.

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0360

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0365

http://refhub.elsevier.com/S1094-9968(13)00035-2/rf0370

Discussion
Appendix A. Measurement Scales
Appendix B. Sequential Logit Model Specification
References

Forster 17

Advertising on Social Network Sites:
A Structural Equation Modelling Approach

Anant Saxena
Uday Khanna

Abstract
Social networking sites (SNSs) emerged as one of the most powerful media for advertising across the globe. Globally, companies
are shifting a larger pie of their advertising budgets towards social networking sites for better reach and interactive platform. The
companies are also looking at it as a low-cost model, which could reap results in minimum time possible for the targeted ‘Facebook
generation’. These very facts motivate researchers to study the value of advertisements on social networking sites like Facebook,
LinkedIn, Twitter and others. The article is an empirical study to understand the implications of different variables in advertisements
on the delivery of advertising value to the respondents. Confirmatory factor analysis (CFA) has been conducted to test the reliability
of instrument being used for data collection. Further, a model has been proposed for measuring advertising value through structural
equation modelling. The predicted results confirm the roles of different variables, namely, information, entertainment and irritation, in
accessing value of advertisements displayed on social networking sites.

Key Words
Advertising Value, Social Networking Sites, Structural Equation Modelling

Introduction
Social networking websites (SNSs) have emerged as the
‘need of an hour’. Their journey started with the launch of
sixdegrees.com in the year 1997, which attracted millions
of users at that time. The site allowed the users to create
profiles listing their friends with the ability to surf the
friends list (Boyd and Ellison, 2007). This has been
followed by an array of SNSs like Facebook, Orkut,
Linkedin and MySpace in the year 2003–2004. Within a
short span of time, these websites become an addiction for
youngsters as these give them opportunity and platform to
express their feelings and emotions in the society. Websites
like Facebook, Orkut, Twitter and MySpace have become
household names and an integral part of people’s life so
much that it has become tough for regular users to imagine
a life without them. Globally, Internet users spend more
than four and a half hours per week on SNSs, more time
than they spend on e-mail (Anderson et al., 2011). As
more and more of what people think and do ends up getting
expressed on SNSs, it is expected that SNSs affect the
buying decisions greatly. In addition, the huge viewer’s
base of these websites makes them a favourable media for
advertisements by different companies. According to a
study done by comScore, Inc., a market research firm,
SNSs accounted for more than 20 per cent, that is, one in

five, display ads of all display ads viewed online, with
Facebook and MySpace combining to deliver more than 80
per cent of ads among sites in the social networking
category (comScore, 2009). According to Rizavi et al.
(2011) social networking websites act as a good platform
for advertising that attract millions of users from different
countries, speaking multiple languages belonging to
different demographics. According to Trusov et al. (2009)
referrals and recommendations on SNSs have a significant
impact on new customer acquisition and retention. This
fact led marketers to turn to Internet platforms like SNSs,
blogs and other social media as an avenue for cost-effective
marketing, employing e-mail campaigns, website adver-
tisements and viral marketing. Also from a marketing
perspective, these websites give potential customers the
opportunity to virtually explore a business, encourage
them to visit and at last share their views and experiences
with their friends (Phillips et al., 2010). Understanding
the effectiveness of SNSs in promoting product and
services through advertisements, companies across the
globe have increased their advertising budget for SNSs
which has led to increase in revenue generation for social
networking website companies. According to a report
released by Interactive Advertising Bureau (IAB),
Internet advertising revenues totaled $14.9 billion in 2011,
up 23 per cent from the $12.1 billion reported in 2010

Vision
17(1) 17–25
© 2013 MDI

SAGE Publications
Los Angeles, London,

New Delhi, Singapore,
Washington DC

DOI: 10.1177/0972262912469560
http://vision.sagepub.com

Article

http://crossmark.crossref.org/dialog/?doi=10.1177%2F0972262912469560&domain=pdf&date_stamp=2013-03-06

18 Advertising on Social Network Sites

Vision, 17, 1 (2013): 17–25

(PricewaterhouseCoopers LLP., 2011). India shares the
same story in terms of Internet advertising revenues.

According to a report, the size of Internet advertising
industry was INR 7.7 billion in 2010 registering a
growth of 28.3 per cent over INR 6 billion in 2009
(PricewaterhouseCoopers Private Limited, 2011). The
same report highlights that in India SNSs have shown a
remarkable growth of 43 per cent in 2010 over 2009,
with a 54 per cent growth in advertising on SNSs in 2010–
2011 (PricewaterhouseCoopers Private Limited, 2011).
Considering the fact that advertising on SNSs is on a new
high, this research focus on studying the value of
advertisements being displayed on SNSs.

Literature Review and Hypothesis
Web advertising continues to be a major area of advertising
research from a long time. A number of studies have been
done discussing advertisements on the Web and their
effects. Berthon et al. (1996) have discussed the role of
World Wide Web as an advertising medium in the mar-
keting communication mix and proved that World Wide
Web is a new medium for advertising characterized by
ease-of-entry, relatively low set-up costs, globalness, time
independence and interactivity. In spite of the acceptance
of World Wide Web as an effective media for advertising,
few studies have focused on the value of advertisements
displayed on this medium. R.H. Ducoffe introduced the
concept of advertisement value in 1995. According to
Ducoffe (1995) advertising value is defined as the utility or
worth of the advertisement. Ducoffe (1996), in his another
study on World Wide Web, proved the significant impact
(either +ve or –ve) of entertainment, information and
irritation on advertisement value. Brackett and Carr (2001)
in their study on cyberspace advertising reports that
information, entertainment, irritation and credibility
significantly affect advertisement value which in turn
affects attitude towards advertisements. Discussion on
different predictors of advertisement value with reference
to SNSs advertisements is hereby illustrated:

1. Information: Information content is an important
determinant of advertisement effectiveness. Comp-
anies advertise for one main reason—providing
information about their product, services and brand
to consumers. Consumers reported that supplying
information is the primary reason why they approve
advertising (Bauer et al., 1968). According to Norris
(1984) information in advertisements enables the
customers to evaluate the products more rationally
leading to improved markets with low prices and
high quality of the product. Information content on
Internet can be delivered better in comparison to

television medium, reason being short time span of
television advertisements. Yoon and Kim (2001)
mentioned that Internet advertising differs from tra-
ditional advertising as it delivers unlimited informa-
tion beyond time and space and it gives unlimited
amount and sources of information. Web advertise-
ments provide information and generate awareness
without interactive involvement (Berthon et al.,
1996). On the contrary, information delivered
through SNSs advertisements is different from tra-
ditional Web advertisements because SNSs provide
a medium that is interactive in nature. A person
could scan and share information with online friends
and followers, thus making the advertisement infor-
mation viral in nature. Large media companies have
realized the potential of SNSs to reach and deepen
relationships with the ‘subscribed’ audience (Jhih-
Syuan and Pena, 2011). This specialty of SNSs
advertisement makes it the most competitive plat-
form for sharing information about products and
services. As the delivery and importance of infor-
mation for SNSs advertisements is different from
other forms of advertisements, it is important to note
its effect on advertisement value. Based on this
rationale, the hypothesis tested is:

H1: There is a significant positive impact of infor-
mation content of advertisements on the value of
advertisements displayed on social networking
websites.

2. Entertainment: An advertisement that is full of
information but nil in entertainment content is not
worthy. According to McQuail (1994) an advertise-
ment entertains when it fulfils the audience needs
for escapism, diversion, aesthetic enjoyment or
emotional release. The ability of advertising to
entertain can enhance the experience of advertising.
In addition, an advertisement could be information
for one and entertainment for other person at the
same time (Alwitt and Prabhaker, 1992). Consumers
who found advertising to be entertaining also evalu-
ate it as informative (Ducoffe, 1995). This shows
that entertainment and information are interrelated
concepts when talking about advertisements.
SNSs platform is interactive in nature and display
banner advertisements of different brands at the
same platform and same time; they have the power
to entertain the audience. Kim and Lee (2010)
noted that college students use SNSs for six
main reasons: entertainment, passing time, social
interaction, information seeking, information provi-
ding, and professional advancement. According to

Anant Saxena and Uday Khanna 19

Vision, 17, 1 (2013): 17–25

Taylor et al. (2011) SNSs advertisements provide
entertainment value to the audience. The same study
reported that entertainment exhibits almost four
times more strength of influence on favourable con-
sumers’ attitude towards advertisements than infor-
mation. With reference to the existing literature, it is
important to find the impact of entertainment on
advertisement value of SNSs advertisements. In the
same vein, the hypothesis tested is:

H2: There is a significant positive impact of enter-
tainment content of advertisements on the value of
advertisements displayed on social networking
websites.

3. Irritation: Irritation from advertisements arises
when we feel discomfort in watching advertisement
due to any reason. The reason can be personal or
social. A personal reason could be distraction while
focusing on a particular task on World Wide Web.
According to Wells et al. (1971) irritation is one
amongst six dimensions of personal reactions
towards advertising. It is the degree to which the
viewer disliked the contents that he had seen. The
words that came into the mind of the viewer at time
of getting irritated from an advertisement are
‘terrible’, ‘stupid’, ‘ridiculous’, ‘irritating’ and
‘phony’. An advertisement can be rewarding for
some viewers and yet be an irritant and unrewarding
for others (Alwitt and Prabhaker, 1992). According
to Aaker and Bruzzone (1985), increase in irritation
can lead to general reduction in the effectiveness of
advertisement. In case of Internet advertising, it also
generates considerable irritation (Schlosser et al.,
1999). As online behaviour including use of SNSs
is highly goal oriented, advertisements on SNSs
might irritate the user (Taylor et al., 2011). The lit-
erature suggested that irritation has a negative effect
on the effectiveness of advertisement irrespective of
the media. Based on this rationale the hypothesis
tested is:

H3: There is a significant negative impact of irrita-
tion content of advertisements on the value of adver-
tisements displayed on social networking websites.

A considerable amount of research on determinants of
Web advertising effectiveness and value has been done
(Berthon et al., 1996; Brown et al., 2007; Ducoffe, 1995;
Lei, 2000; Schlosser et al., 1999; Yoon and Kim, 2001);
however, these studies were more focused on traditional
websites rather than SNSs. Advertising through SNSs is
different from traditional websites due to several reasons.

First, advertisements on SNSs are different not only in form
and substance but also in delivery method. Some of the
messages are ‘pushed’ upon consumers while others rely on
consumers to ‘pull’ content; some generate revenue whereas
some are non-paid content delivered through media content
(Taylor et al., 2011). Second, SNSs have their own unique
user-to-user interface (Safko and Brake, 2009). Third, SNSs
users are increasing day by day all over the world, which
makes this medium suitable for advertising. As SNSs
advertising is different from traditional Web advertising and
a little is known about value of SNSs advertisements, this
study tries to fill this research gap by providing a model,
which tests the interrelationships between different
determinants of advertisement value.

Model Testing
The importance of advertisements displayed on SNSs is
increasing day by day. According to Stelzner (2011) 88 per cent
of the marketers have reported that their social media
advertisements have generated more exposure for their
businesses. This leads the authors to test a model for accessing
the value of advertisements displayed on SNSs by employing
structural equation modelling (SEM) approach. Use of SEM
technique gives us the opportunity to examine multiple
dependence techniques simultaneously. SEM approach is a
statistical methodology that combines the strength of factor
analysis and path analysis. According to Singh (2009) SEM is
considered as a more advanced technique than other multivariate
techniques because it can estimate a series of interrelated
dependence relationship simultaneously. According to Byrne
(1998) SEM technique is better because:

1. It accounts for measurement errors in course of
model testing.

2. It can incorporate observed (indicator) variables as
well as latent (unobserved) variables at same time
during model testing,

3. It tests a priori relationships rather than allowing the
technique or data to define the nature of relationship
between the variables.

In the present study, SEM analysis is conducted in
two major steps; first, to test the measurement model
and second, a structural model. Measurement model
provides the series of relationships that suggests how
observed variables represent latent variables (Figure 1),
tested by means of confirmatory factor analysis (CFA).
Structural model tests the conceptual representation of the
relationships between the latent variables. It tells whether
the proposed model is eligible to represent a proposed
concept and conceptual relationships between the variables
or not (Figure 2).

20 Advertising on Social Network Sites

Vision, 17, 1 (2013): 17–25

Figure 1. Measurement Model

ENTERTAINMENT

INFORMATION

IRRITATION

ADD.VALUE

ENTERTAIN 3

ENTERTAIN 2

ENTERTAIN 1

INFO 1

INFO 2

INFO 3

IRRITATION 1

IRRITATION 2

IRRITATION 3

ADDVALUE 1

ADDVALUE 2

ADDVALUE 3

0.57

0.17

0.19

0.47

0.40

0.51

0.45

0.80

0.39

0.80

0.86

0.52

0.23

0.75

0.65

0.76

0.77

0.73

Anant Saxena and Uday Khanna 21

Vision, 17, 1 (2013): 17–25

Figure 2. Structural Model

Method
Sample Design

The research focuses on social networking websites with
college students as sample respondents. The college students
were selected as sample for two basic reasons. First,
student sample is more homogeneous (less variable) in
terms of socio-economic background, demographics and
education (Peterson, 2001). Second, a number of studies
have reported that students are the main users of social
networking websites (Dwyer et al., 2007; Pempek et al.,
2009; Subrahmanyam et al., 2008). With this rationale,
present study sample includes postgraduate management
students of a reputed college based in India. 276 students
have responded to an online questionnaire mailed to 300
students. The questionnaires were mailed with Google
documents facility to form and mail online forms/
questionnaires. After removing incomplete questionnaires,
only 189 questionnaires were found to be useable for analysis
and further study. Resulting sample consists of 71 per cent
males and 29 per cent females. Subjects were asked to report
their reactions to instrument statements by considering their

perceptions of advertisements on SNSs in general, not a
single advertisement or advertisement for any particular
product. The objective of this generalization is to assess the
value of advertisement on social networking websites across
different advertisements of product and service categories.

Sample Size and SEM Analysis

Sample size is a key issue when performing SEM analysis.
According to Bentler and Bonett (1980) and Hair et al.
(2007) chi-square value is sensitive to increase in sample
size, while it lacks power to discriminate between good fit
and poor fit models with small sample size (Kenny and
McCoach, 2003). Hair et al. (2007) mentioned that 15 res-
ponses per parameter is an appropriate ratio for sample
size. Going on with this approach a sample size of 189 res-
pondents for measuring 12 parameters was appropriate.

Research Instrument

For measuring the advertisement value of advertisements
displayed on social media, a 12 item scale developed by

ADD. VALUE

ADDVALUE 3
ADDVALUE 2
ADDVALUE 1

IN1 IN2 IN3

e3 e2 e1

IRR1 IRR2 IRR3

e9 e8 e7

EN1 EN2 EN3

e6 e5 e4

INFORMATION
IRRITATION
ENTERTAINMENT

e10

e11

e13

e12

0.55 0.84 0.38

0.27

0.15

0.87 0.58

0.77 0.90 0.51

0.40

0.36
0.38

0.75
0.76

0.72

0.25

22 Advertising on Social Network Sites

Vision, 17, 1 (2013): 17–25

Ducoffe (1995) was used. The instrument was modified as
per the need of the study. A five-item Likert scale was used as
a response scale, from strongly disagrees to strongly agree.

Measurement Model
Measurement model is a specification of the measurement
theory that shows how constructs are operationalized by
sets of measured items. Confirmatory factor analysis is
used to test the reliability of a measurement model. Unlike
exploratory factor analysis, CFA allows the researcher to
tell the SEM programme which variable belongs to which
factor before the analysis (Hair et al., 2007). According to
Salisbury et al. (2001) CFA allows the researcher to specify
the actual relationship between the items and factors as
well as linkages between them.

Construct Validity

According to Hair et al. (2007) construct validity is the
extent to which a set of measured items actually represents
theoretical latent construct; those items are designed to
measure. The reliability of advertisement value scale was
examined by specifying a model in CFA using AMOS 19.
Reliability of an instrument can also be calculated by
Cronbach’s alpha, but use of SEM technique makes
such a practice unnecessary and redundant (Bagozzi
and Yi, 2012). The results (see Table 1) confirm the
overall fit of a measurement model when employed to
CFA.

According to Hair et al. (2007) one incremental fit index
(CFI), one goodness of fit index (GFI), one absolute fit
index (GFI, SRMR) and one badness of fit index (SRMR),
with chi-square statistic should be used to assess a model’s
goodness of fit. Our study results show all the different
types of indices in the acceptable range.

Convergent and Discriminant Validity

Convergent validity exists when the items that are
indicators of a specific construct converge or share a high
proportion of variance in common. In general, ‘factor
loading’ and ‘variance extracted’ measures are used to
measure convergent validity. We have used factor loading
measure in our study to measure convergent validity (Hair
et al., 2007; Salisbury et al., 2001). All the factor loadings
are statistically significant, a minimum requirement for
convergence (Hair et al., 2007). Furthermore, except items
‘Info 3’ and ‘Irritation 1’ all factor loadings are in the range
of 0.50 to 0.80, which is more than acceptable value of
0.50 (Hair et al., 2007) (see Figure 1). According to Chin
et al. (1997) discriminant validity exists if the correlation
between the constructs is not equal to 1. Following the rule,
our study shows the discriminant validity between the
constructs (see Figure 1).

Structural Model
After assessing the eligibility of scale for measuring
different variables in the study, the next step is to test the
hypothesized relationships in a structural model. Ducoffe
(1996) has proved the respective role of information,
entertainment and irritation on advertisement value for the
advertisements on the Web. In our study, we try to explore
the impact of these respective variables on advertisement
value vis-à-vis SNSs.

Performance of the Model

Hypothesized relationships are supported by the overall
model fit indices obtained. All of the fit indices are above
the recommended values. The c2/df value 2.31 met the
recommended value of less than 3 (Carmines and McIver,
1981). Hair et al. (2007) argues that chi-square value is
sensitive to sample size and number of variables; therefore,
c2/df value is not taken as a sole indicator of model fit.
Other model fit indicators taken are also within the
recommended range (see Table 2). In sum, various model
fit indices indicates that the proposed model fitted well
with the present data set.

Table 1. Model Fit Indices for Measurement Model

Statistic
Recommended

Value Obtained Value

Chi-square c2 92.616
Df 48
c2/df (Hinkin, 1995),

(Carmines and
McIver, 1981)

< 3.00 1.93

GFI (Hooper et al.,
2008), (Hair et al.,
2007)

> 0.90 0.92

AGFI (Muenjohn and
Armstrong, 2008)

> 0.80 0.88

SRMR (Hu and Bentler,
1999)

< 0.08 0.06

CFI (Watchravesringkan
et al., 2008)

> 0.80 0.92

Note: AGFI: Adjusted goodness of fit index; SRMR: Standardized root
mean square residual; CFI: Comparative fit index

Anant Saxena and Uday Khanna 23

Vision, 17, 1 (2013): 17–25

Table 2. Model Fit Indices for Structural Model

Statistic
Recommended
Value Obtained Value

Chi-square c2 115.539
Df 50
c2/df (Hinkin, 1995),

(Carmines and McIver,
1981)

< 3.00 2.31

GFI (Hooper et al., 2008),
(Hair et al., 2007)

> 0.90 0.91

AGFI (Muenjohn and
Armstrong, 2008)

> 0.80 0.86

RMSEA (MacCallum et al.,
1996)

< 0.10 0.08

CFI (Watchravesringkan
et al., 2008)

> 0.80 0.88

Note: SMSEA: Root Mean Square Error of Approximation

Estimated Standardized Path Coefficients

Figure 2 shows the standardized path coefficients of
the four constructs under investigation. All the path
coefficients were significant at the level of 0.01 with the
direction of influence as hypothesized (+ or −). Information
and entertainment were positively associated with
advertisement value whereas irritation is negatively asso-
ciated with advertisement value; thus all the hypotheses
framed are statistically supported. A significant correlation
between information and entertainment also indicates that
the consumers who find advertisement to be entertaining
are more likely to evaluate it as informative. These results
are consistent with another study (Ducoffe, 1995). Finally,
the squared multiple correlations (R2) indicates that the
present model explains 38 per cent of the variance in
advertisement value.

Discussion and Implication
The study yielded important new insights about a topic that
is important for both industry practitioners and aca-
demicians. The concept of advertisement value and factors
affecting it had been widely tested for various types
of advertisements in a number of studies but lack of work
for advertisements displayed on social networking websites
was the motivating factor to do research in the particular
domain. The study tests the model to assess advertisement
value by employing SEM approach. SEM combines the
strength of factor analysis and path analysis. It enables us
to test whether observed variables completely describes
latent variables or not. In addition, SEM is a more
successful technique than other multivariate techniques as
it can estimate a series of interrelated dependence
relationship simultaneously. It tells whether the proposed

model is eligible to represent a proposed concept and
conceptual relationships between the variables or not. The
results of CFA suggest that the observed variables are
suitable enough to represent different latent variables, that
is, information, entertainment, irritation and advertisement
value in the particular domain of social networking
advertising.

The findings of structural model analysis suggest that
the proposed model for accessing the value of
advertisements displayed on SNSs fits well. In addition,
the proposed hypotheses assessing the relationships
between the variables are statistically supported. The
findings suggest that when advertisements displayed on
SNSs provide entertainment and information content or
impressions, it increases the worth of the advertisement.
On the one hand, as has been proved true for other types of
media advertising, consumers derive utility from
advertisements that provide some useful or functional
information and increase hedonic value by entertaining
them. On the other hand, irritation decreases the net worth
of the advertisements displayed on SNSs. This suggests
that the companies using SNSs media for advertising their
products and services should reduce the contents, which
irritate the viewers’ base.

It is worth noting that ‘information’ exhibited around
1.6 times more strength of influence on advertisement
value than entertainment. This suggests that companies
should firstly focus on providing information content in
their advertisements to make their advertisements worth
for consumers. In addition, it is interesting to note that
findings of this study show a significant correlation
between information and entertainment, which indicates
that consumers who find advertisement to be entertaining
are more likely to evaluate it as informative.

Limitations
Although the study has been done taking into account the
methodological rigour, some limitations remain. First, the
sampling used is convenience sampling. Second,
exploration of other variables that affects the value of
advertisement is needed.

References
Aaker, D.A., & Bruzzone, D.E. (1985). Causes of irritation in

advertising. The Journal of Marketing, 49(2), 47–57.
Alwitt, L.F., & Prabhaker, P.R. (1992). Functional and belief

dimensions of attitudes to television advertising: Implications
for copytesting. Journal of Advertising Research, 32(5),
30–42.

Anderson, M., Brusa, J., Price, J., & Sims, J. (2011). Turning
‘Like’ to ‘Buy’ social media emerges as a commerce

24 Advertising on Social Network Sites

Vision, 17, 1 (2013): 17–25

channel (pp. 1–9). Booz & Company. Available at http://
www.booz.com/media/uploads/BaC-Turning_Like_to_Buy.
pdf

Bagozzi, R., & Yi, Y. (2012). Specification, evaluation, and
interpretation of structural equation models. Journal of the
Academy of Marketing Science, 40(1), 8–34.

Bauer, R.A., Greyser, S.A., Kanter, D.L., & Weilbacher, W.M.
(1968). Advertising in America: The consumer view. Division
of Research, Graduate School of Business Administration,
Harvard University Boston.

Bentler, P.M., & Bonett, D.G. (1980). Significance tests and
goodness of fit in the analysis of covariance structures.
Psychological Bulletin, 88(3), 588–606.

Berthon, P., Pitt, L.F., & Watson, R.T. (1996). The World Wide
Web as an advertising medium: Toward an understanding
of conversion efficiency. Journal of Advertising Research,
36(1), 43–54.

Boyd, Danah M., & Ellison, N.B. (2007). Social network sites:
Definition, history, and scholarship. Journal of Computer-
Mediated Communication, 13(1), 210–230.

Brackett, L.K., & Carr, B.N. (2001). Cyberspace advertising vs.
other media: Consumer vs. mature student attitudes. Journal
of Advertising Research, 41(5), 23–32.

Brown, J., Broderick, A.J., & Lee, N. (2007). Word of mouth
communication within online communities: Conceptualizing
the online social network. Journal of Interactive Marketing,
21(3), 2–20.

Byrne, B.M. (1998). Structural equation modeling with Lisrel,
Prelis, and Simplis: Basic concepts, applications, and pro-
gramming. New Jersey: Routledge.

Carmines, E.G., & McIver, J.P. (1981). Analyzing models with
unobserved variables: Analysis of covariance structures. In
Social measurement: Current issues (pp. 65–115). Beverly
Hills: SAGE.

Chin, W.W., Gopal, A., & Salisbury, W.D. (1997). Advancing
the theory of adaptive structuration: The development of a
scale to measure faithfulness of appropriation. Information
Systems Research, 8(4), 342–367.

Ducoffe, R.H. (1995). How consumers assess the value of adver-
tising. Journal of Current Issues & Research in Advertising,
17(1), 1–18.

———. (1996). Advertising value and advertising the Web.
Journal of Advertising Research, 36(5), 21–35.

Dwyer, C., Hiltz, S.R., & Passerini, K. (2007). Trust and
privacy concern within social networking sites: A compari-
son of Facebook and MySpace. AMCIS 2007 Proceedings
(pp. 1–12). Presented at the Thirteenth Americas Conference
on Information Systems, 9–12 August 2007, Colorado, USA.
Retrieved from http://aisel.aisnet.org/amcis2007/339

Hair, J., Black, W., Babin, B., Anderson, R., & Tatham,
R. (2007). Multivariate data analysis (6th edn). New Delhi:
Pearson Education India.

Hinkin, T.R. (1995). A review of scale development practices in
the study of organizations. Journal of Management, 21(5),
967–988.

Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural
equation modelling: Guidelines for determining model fit.
Articles, 6(1), 53–60.

Hu, L., & Bentler, P.M. (1999). Cutoff criteria for fit indexes
in covariance structure analysis: Conventional criteria
versus new alternatives. Structural Equation Modeling: A
Multidisciplinary Journal, 6(1), 1–55.

Jhih-Syuan Lin, & Peña, J. (2011). Are you following me? A
content analysis of TV networks’ brand communication on
Twitter. Journal of Interactive Advertising, 12(1), 17–29.

Kenny, D.A., & McCoach, D.B. (2003). Effect of the number of
variables on measures of fit in structural equation modeling.
Structural Equation Modeling, 10(3), 333–351.

Kim, M., & Lee, M. (2010). Why do college students use Twitter?
Presented at the Association for Education in Journalism and
Mass Communication Annual Conference, 4–7 August 2007,
Denver, CO.

Lei, R.M. (2000). An assessment of the World Wide Web as an
advertising medium. Social Science Journal, 37(3), 465.

MacCallum, R.C., Browne, M.W., & Sugawara, H.M. (1996).
Power analysis and determination of sample size for covari-
ance structure modeling. Psychological Methods, 1(2), 130.

McQuail, D. (1994). Mass communication theory: An introduc-
tion. London, UK: SAGE.

Muenjohn, D.N., & Armstrong, P.A. (2008). Evaluating the
structural validity of the multifactor leadership questionnaire
(MLQ), capturing the leadership factors of transformational-
transactional leadership. Contemporary Management
Research, 4(1), 3–14.

Norris, V.P. (1984). The economic effects of advertising: A
review of the literature. Current Issues & Research in
Advertising, 7(2), 39.

Pempek, T.A., Yermolayeva, Y.A., & Calvert, S.L. (2009).
College students’ social networking experiences on
Facebook. Journal of Applied Developmental Psychology,
30(3), 227–238.

Peterson, A.R. (2001). On the use of college students in social
science research: Insights from a second-order meta-analysis.
Journal of Consumer Research, 28(3), 450–461.

Phillips, M., McFadden, D.T., & Sullins, M. (2010). How effec-
tive is social networking for direct marketers? Journal of
Food Distribution Research, 41(1), 96–100.

PricewaterhouseCoopers LLP. (2011). IAB Internet advertising
revenue report (pp. 1–23). New York: Interactive Advertising
Bureau.

PricewaterhouseCoopers Private Limited. (2011). India
entertainment and media outlook (pp. 1–141). Kolkata:
PricewaterhouseCoopers Private Limited.

Rizavi, S.S., Ali, L., & Rizavi, S.H.M. (2011). User perceived
quality of social networking websites: A study of Lahore
region. Interdisciplinary Journal of Contemporary Research
in Business, 2(12), 902–913.

Safko, L., & Brake, D.K. (2009). The social media bible: Tactics,
tools, and strategies for business success. New Jersey: John
Wiley & Sons.

Salisbury, W.D., Pearson, R.A., Pearson, A.W., & Miller, D.W.
(2001). Perceived security and World Wide Web purchase inten-
tion. Industrial Management & Data Systems, 101(3/4), 165.

Schlosser, A.E., Shavitt, S., & Kanfer, A. (1999). Survey of
Internet users’ attitudes toward Internet advertising. Journal
of interactive marketing, 13(3), 34–54.

Anant Saxena and Uday Khanna 25

Vision, 17, 1 (2013): 17–25

Singh, R. (2009). Does my structural model represent the real
phenomenon? A review of the appropriate use of structural
equation modelling (SEM) model fit indices. The Marketing
Review, 9(3), 199–212.

Social Networking Sites Account for More than 20 Percent of
All U.S. Online Display Ad Impressions, According to com-
Score Ad Metrix – comScore, Inc. (2009, 1 September).
Retrieved from http://www.comscore.com/Press_Events/
Press_Releases/2009/9/Social_Networking_Sites_Account_
for_More_than_20_Percent_of_All_U.S._Online_Display_
Ad_Impressions_According_to_comScore_Ad_Metrix
(accessed on 14 December 2012).

Stelzner, M. (n.d.). 2011 Social Media Marketing Industry Report |
Social Media Examiner. Retrieved from http://www.
socialmediaexaminer.com/social-media-marketing-industry-
report-2011/

Subrahmanyam, K., Reich, S.M., Waechter, N., & Espinoza, G.
(2008). Online and offline social networks: Use of social
networking sites by emerging adults. Journal of Applied
Developmental Psychology, 29(6), 420–433.

Taylor, D.G., Lewin, J.E., & Strutton, D. (2011). Friends, fans,
and followers: Do ads work on social networks? How gender
and age shape receptivity. Journal of Advertising Research,
51(1), 258–275.

Trusov, M., Bucklin, R.E., & Pauwels, K. (2009). Effects of
word-of-mouth versus traditional marketing: Findings from
an Internet social networking site. Journal of Marketing,
73(5), 90–102.

Watchravesringkan, K.T., Yan, R.N., & Yurchisin, J. (2008).
Cross-cultural invariance of consumers’ price perception

measures: Eastern Asian perspective. International Journal
of Retail & Distribution Management, 36(10), 759–779.

Wells, W.D., Leavitt, C., & McConville, M. (1971). A reaction
profile for TV commercials. Journal of Advertising Research,
11(6), 11–18.

Yoon Sung-Joon, & Kim Joo-Ho. (2001). Is the Internet more
effective than traditional media? Factors affecting the choice
of media. Journal of Advertising Research, 41(6), 53–60.

Anant Saxena (asaxena@imt.edu) is working as a Research
Associate at IMT Ghaziabad, UP, India. He is currently
researching the role of common service center (CSC) project in
Indian governance and also working on the impact of green
marketing on consumer purchase decision in India. He
has published research papers in national and international
journals of repute. His research interests are marketing through
social media, information technology & government policies
and e-marketing.

Uday Khanna (khannauday77@gmail.com) is an Assistant
Professor at the Faculty of Management Studies at Graphic Era
University, Dehradun, India. His areas of interest are Marketing,
Marketing Research and Sales and Distribution. He is currently
researching the quality of corporate governance of Indian
companies. He has published some good papers in national and
international journals of repute. He has rich industry experience
in FMCG companies of repute like Gillette India Ltd and
Hindustan Pencils Ltd.

Journal of Marketing Research
Vol. XLIX (April 2012), 192 –205

*Jonah Berger is Joseph G. Campbell Assistant Professor of Marketing
(e-mail: jberger@wharton.upenn.edu), and Katherine L. Milkman is Assistant
Professor of Operations and Information Management (e-mail: kmilkman@
wharton.upenn.edu), the Wharton School, University of Pennsylvania.
Michael Buckley, Jason Chen, Michael Durkheimer, Henning Krohnstad,
Heidi Liu, Lauren McDevitt, Areeb Pirani, Jason Pollack, and Ronnie
Wang all provided helpful research assistance. Hector Castro and Premal
Vora created the web crawler that made this project possible, and Roger
Booth and James W. Pennebaker provided access to LIWC. Devin Pope
and Bill Simpson provided helpful suggestions on our analysis strategy.
Thanks to Max Bazerman, John Beshears, Jonathan Haidt, Chip Heath,
Yoshi Kashima, Dacher Keltner, Kim Peters, Mark Schaller, Deborah
Small, and Andrew Stephen for helpful comments on prior versions of the
article. The Dean’s Research Initiative and the Wharton Interactive Media
Initiative helped fund this research. Ravi Dhar served as associate editor
for this article.

JONAH BERGER and KATHERINE L. MILKMAN*

Why are certain pieces of online content (e.g., advertisements, videos,
news articles) more viral than others? This article takes a psychological
approach to understanding diffusion. Using a unique data set of all the
New York Times articles published over a three-month period, the authors
examine how emotion shapes virality. The results indicate that positive
content is more viral than negative content, but the relationship between
emotion and social transmission is more complex than valence alone.
Virality is partially driven by physiological arousal. Content that evokes
high-arousal positive (awe) or negative (anger or anxiety) emotions is
more viral. Content that evokes low-arousal, or deactivating, emotions
(e.g., sadness) is less viral. These results hold even when the authors
control for how surprising, interesting, or practically useful content is (all
of which are positively linked to virality), as well as external drivers of
attention (e.g., how prominently content was featured). Experimental
results further demonstrate the causal impact of specific emotion on
transmission and illustrate that it is driven by the level of activation
induced. Taken together, these findings shed light on why people share
content and how to design more effective viral marketing campaigns.

Keywords: word of mouth, viral marketing, social transmission, online
content

What Makes Online Content Viral?

Sharing online content is an integral part of modern life.
People forward newspaper articles to their friends, pass
YouTube videos to their relatives, and send restaurant
reviews to their neighbors. Indeed, 59% of people report that
they frequently share online content with others (Allsop,
Bassett, and Hoskins 2007), and someone tweets a link to a
New York Times story once every four seconds (Harris 2010).
Such social transmission also has an important impact on

both consumers and brands. Decades of research suggest

that interpersonal communication affects attitudes and deci-
sion making (Asch 1956; Katz and Lazarsfeld 1955), and
recent work has demonstrated the causal impact of word of
mouth on product adoption and sales (Chevalier and Mayz –
lin 2006; Godes and Mayzlin 2009).
Although it is clear that social transmission is both fre-

quent and important, less is known about why certain pieces
of online content are more viral than others. Some customer
service experiences spread throughout the blogosphere,
while others are never shared. Some newspaper articles earn
a position on their website’s “most e-mailed list,” while oth-
ers languish. Companies often create online ad campaigns
or encourage consumer-generated content in the hope that
people will share this content with others, but some of these
efforts take off while others fail. Is virality just random, as
some argue (e.g., Cashmore 2009), or might certain charac-
teristics predict whether content will be highly shared?
This article examines how content characteristics affect

virality. In particular, we focus on how emotion shapes
social transmission. We do so in two ways. First, we analyze
a unique data set of nearly 7000 New York Times articles to
examine which articles make the newspaper’s “most e-
mailed list.” Controlling for external drivers of attention,
such as where an article was featured online and for how
long, we examine how content’s valence (i.e., whether an

http://crossmark.crossref.org/dialog/?doi=10.1509%2Fjmr.10.0353&domain=pdf&date_stamp=2012-04-01

What Makes Online Content Viral? 193

article is positive or negative) and the specific emotions it
evokes (e.g., anger, sadness, awe) affect whether it is highly
shared. Second, we experimentally manipulate the specific
emotion evoked by content to directly test the causal impact
of arousal on social transmission.
This research makes several important contributions. First,

research on word of mouth and viral marketing has focused
on its impact (i.e., on diffusion and sales; Godes and May-
zlin 2004, 2009; Goldenberg et al. 2009). However, there has
been less attention to its causes or what drives people to share
content with others and what type of content is more likely
to be shared. By combining a large-scale examination of real
transmission in the field with tightly controlled experiments,
we both demonstrate characteristics of viral online content
and shed light on the underlying processes that drive people
to share. Second, our findings provide insight into how to
design successful viral marketing campaigns. Word of mouth
and social media are viewed as cheaper and more effective
than traditional media, but their utility hinges on people
transmitting content that helps the brand. If no one shares a
company’s content or if consumers share content that por-
trays the company negatively, the benefit of social transmis-
sion is lost. Consequently, understanding what drives peo-
ple to share can help organizations and policy makers avoid
consumer backlash and craft contagious content.

CONTENT CHARACTERISTICS AND SOCIAL
TRANSMISSION

One reason people may share stories, news, and informa-
tion is because they contain useful information. Coupons or
articles about good restaurants help people save money and
eat better. Consumers may share such practically useful
content for altruistic reasons (e.g., to help others) or for self-
enhancement purposes (e.g., to appear knowledgeable, see
Wojnicki and Godes 2008). Practically useful content also has
social exchange value (Homans 1958), and people may share
it to generate reciprocity (Fehr, Kirchsteiger, and Riedl 1998).
Emotional aspects of content may also affect whether it is

shared (Heath, Bell, and Sternberg 2001). People report dis-
cussing many of their emotional experiences with others,
and customers report greater word of mouth at the extremes
of satisfaction (i.e., highly satisfied or highly dissatisfied;
Anderson 1998). People may share emotionally charged con-
tent to make sense of their experiences, reduce dissonance, or
deepen social connections (Festinger, Riecken, and Schachter
1956; Peters and Kashima 2007; Rime et al. 1991).
Emotional Valence and Social Transmission
These observations imply that emotionally evocative

content may be particularly viral, but which is more likely
to be shared—positive or negative content? While there is a
lay belief that people are more likely to pass along negative
news (Godes et al. 2005), this has never been tested. Fur-
thermore, the study on which this notion is based actually
focused on understanding what types of news people
encounter, not what they transmit (see Goodman 1999).
Consequently, researchers have noted that “more rigorous
research into the relative probabilities of transmission of
positive and negative information would be valuable to both
academics and managers” (Godes et al. 2005, p. 419).
We hypothesize that more positive content will be more

viral. Consumers often share content for self-presentation

purposes (Wojnicki and Godes 2008) or to communicate
identity, and consequently, positive content may be shared
more because it reflects positively on the sender. Most peo-
ple would prefer to be known as someone who shares
upbeat stories or makes others feel good rather than some-
one who shares things that makes others sad or upset. Shar-
ing positive content may also help boost others’ mood or
provide information about potential rewards (e.g., this
restaurant is worth trying).
The Role of Activation in Social Transmission
Importantly, however, the social transmission of emo-

tional content may be driven by more than just valence. In
addition to being positive or negative, emotions also differ
on the level of physiological arousal or activation they
evoke (Smith and Ellsworth 1985). Anger, anxiety, and sad-
ness are all negative emotions, for example, but while anger
and anxiety are characterized by states of heightened
arousal or activation, sadness is characterized by low
arousal or deactivation (Barrett and Russell 1998).
We suggest that these differences in arousal shape social

transmission (see also Berger 2011). Arousal is a state of
mobilization. While low arousal or deactivation is charac-
terized by relaxation, high arousal or activation is character-
ized by activity (for a review, see Heilman 1997). Indeed,
this excitatory state has been shown to increase action-
related behaviors such as getting up to help others (Gaertner
and Dovidio 1977) and responding faster to offers in nego-
tiations (Brooks and Schweitzer 2011). Given that sharing
information requires action, we suggest that activation
should have similar effects on social transmission and boost
the likelihood that content is highly shared.
If this is the case, even two emotions of the same valence

may have different effects on sharing if they induce differ-
ent levels of activation. Consider something that makes peo-
ple sad versus something that makes people angry. Both
emotions are negative, so a simple valence-based perspec-
tive would suggest that content that induces either emotion
should be less viral (e.g., people want to make their friends
feel good rather than bad). An arousal- or activation-based
analysis, however, provides a more nuanced perspective.
Although both emotions are negative, anger might increase
transmission (because it is characterized by high activation),
while sadness might actually decrease transmission
(because it is characterized by deactivation or inaction).

THE CURRENT RESEARCH
We examine how content characteristics drive social

transmission and virality. In particular, we not only examine
whether positive content is more viral than negative content
but go beyond mere valence to examine how specific emo-
tions evoked by content, and the activation they induce,
drive social transmission.
We study transmission in two ways. First, we investigate

the virality of almost 7000 articles from one of the world’s
most popular newspapers: the New York Times (Study 1).
Controlling for a host of factors (e.g., where articles are fea-
tured, how much interest they evoke), we examine how the
emotionality, valence, and specific emotions evoked by an
article affect its likelihood of making the New York Times’
most e-mailed list. Second, we conduct a series of lab
experiments (Studies 2a, 2b, and 3) to test the underlying

process we believe to be responsible for the observed
effects. By directly manipulating specific emotions and
measuring the activation they induce, we test our hypothe-
sis that content that evokes high-arousal emotion is more
likely to be shared.

STUDY 1: A FIELD STUDY OF EMOTIONS AND
VIRALITY

Our first study investigates what types of New York Times
articles are highly shared. The New York Times covers a
wide range of topics (e.g., world news, sports, travel), and
its articles are shared with a mix of friends (42%), relatives
(40%), colleagues (10%), and others (7%),1 making it an
ideal venue for examining the link between content charac-
teristics and virality. The New York Times continually
reports which articles from its website have been the most
e-mailed in the past 24 hours, and we examine how (1) an
article’s valence and (2) the extent to which it evokes vari-
ous specific emotions (e.g., anger or sadness) affect whether
it makes the New York Times’ most e-mailed list.
Negative emotions have been much better distinguished

from one another than positive emotions (Keltner and
Lerner 2010). Consequently, when considering specific
emotions, our archival analysis focuses on negative emo-
tions because they are straightforward to differentiate and
classify. Anger, anxiety, and sadness are often described as
basic, or universal, emotions (Ekman, Friesen, and Ellsworth
1982), and on the basis of our previous theorizing about
activation, we predict that negative emotions characterized
by activation (i.e., anger and anxiety) will be positively linked
to virality, while negative emotions characterized by deacti-
vation (i.e., sadness) will be negatively linked to virality.
We also examine whether awe, a high-arousal positive

emotion, is linked to virality. Awe is characterized by a feeling
of admiration and elevation in the face of something greater
than oneself (e.g., a new scientific discovery, someone over-
coming adversity; see Keltner and Haidt 2003). It is gener-
ated by stimuli that open the mind to unconsidered possibil-
ities, and the arousal it induces may promote transmission.
Importantly, our empirical analyses control for several

potentially confounding variables. First, as we noted previ-
ously, practically useful content may be more viral because it
provides information. Self-presentation motives also shape
transmission (Wojnicki and Godes 2008), and people may
share interesting or surprising content because it is entertain-
ing and reflects positively on them (i.e., suggests that they
know interesting or entertaining things). Consequently, we
control for these factors to examine the link between emotion
and virality beyond them (though their relationships with
virality may be of interest to some scholars and practitioners).
Second, our analyses also control for things beyond the

content itself. Articles that appear on the front page of the
newspaper or spend more time in prominent positions on
the New York Times’ home page may receive more attention
and thus mechanically have a better chance of making the
most e-mailed list. Consequently, we control for these and
other potential external drivers of attention.2 Including these

controls also enables us to compare the relative impact of
placement versus content characteristics in shaping virality.
While being heavily advertised, or in this case prominently
featured, should likely increase the chance content makes
the most e-mailed list, we examine whether content charac-
teristics (e.g., whether an article is positive or awe-inspiring)
are of similar importance.
Data
We collected information about all New York Times arti-

cles that appeared on the newspaper’s home page (www.
nytimes. com) between August 30 and November 30, 2008
(6956 articles). We captured data using a web crawler that
visited the New York Times’ home page every 15 minutes
during the period in question. It recorded information about
every article on the home page and each article on the most
e-mailed list (updated every 15 minutes). We captured each
article’s title, full text, author(s), topic area (e.g., opinion,
sports), and two-sentence summary created by the New York
Times. We also captured each article’s section, page, and
publication date if it appeared in the print paper, as well as
the dates, times, locations, and durations of all appearances
it made on the New York Times’ home page. Of the articles
in our data set, 20% earned a position on the most e-mailed
list.
Article Coding
We coded the articles on several dimensions. First, we

used automated sentiment analysis to quantify the positivity
(i.e., valence) and emotionality (i.e., affect ladenness) of
each article. These methods are well established (Pang and
Lee 2008) and increase coding ease and objectivity. Auto-
mated ratings were also significantly positively correlated
with manual coders’ ratings of a subset of articles. A com-
puter program (LIWC) counted the number of positive and
negative words in each article using a list of 7630 words clas-
sified as positive or negative by human readers (Pennebaker,
Booth, and Francis 2007). We quantified positivity as the
difference between the percentage of positive and negative
words in an article. We quantified emotionality as the per-
centage of words that were classified as either positive or
negative.
Second, we relied on human coders to classify the extent

to which content exhibited other, more specific characteris-
tics (e.g., evoked anger) because automated coding systems
were not available for these variables. In addition to coding
whether articles contained practically useful information or
evoked interest or surprise (control variables), coders quan-
tified the extent to which each article evoked anxiety, anger,
awe, or sadness.3 Coders were blind to our hypotheses.
They received the title and summary of each article, a web
link to the article’s full text, and detailed coding instructions
(see the Web Appendix at www.marketingpower.com/jmr_
webappendix). Given the overwhelming number of articles
in our data set, we selected a random subsample for coding

194 JOURNAL OF MARKETING RESEARCH, APRIL 2012

1These figures are based on 343 New York Times readers who were asked
with whom they had most recently shared an article.
2Discussion with newspaper staff indicated that editorial decisions about

how to feature articles on the home page are made independently of (and
well before) their appearance on the most e-mailed list.

3Given that prior work has examined how the emotion of disgust might
affect the transmission of urban legends (Heath, Bell, and Sternberg 2001),
we also include disgust in our analysis. (The rest of the results remain
unchanged regardless of whether it is in the model.) While we do not find
any significant relationship between disgust and virality, this may be due
in part to the notion that in general, New York Times articles elicit little of
this emotion.

What Makes Online Content Viral? 195

(n = 2566). For each dimension (awe, anger, anxiety, sad-
ness, surprise, practical utility, and interest), a separate
group of three independent raters rated each article on a
five-point Likert scale according to the extent to which it
was characterized by the construct in question (1 = “not at
all,” and 5 = “extremely”). We gave raters feedback on their
coding of a test set of articles until it was clear that they
understood the relevant construct. Interrater reliability was
high on all dimensions (all ’s > .70), indicating that con-
tent tends to evoke similar emotions across people. We
averaged scores across coders and standardized them (for
sample articles that scored highly on the different dimen-
sions, see Table 1; for summary statistics, see Table 2; and
for correlations between variables, see the Appendix). We
assigned all uncoded articles a score of zero on each dimen-
sion after standardization (i.e., we assigned uncoded articles
the mean value), and we included a dummy in regression
analyses to control for uncoded stories (for a discussion of
this standard imputation methodology, see Cohen and
Cohen 1983). This enabled us to use the full set of articles
collected to analyze the relationship between other content
characteristics (that did not require manual coding) and
virality. Using only the coded subset of articles provides
similar results.

Additional Controls
As we discussed previously, external factors (separate

from content characteristics) may affect an article’s virality
by functioning like advertising. Consequently, we rigor-
ously control for such factors in our analyses (for a list of
all independent variables including controls, see Table 3).
Appearance in the physical newspaper. To characterize

where an article appeared in the physical newspaper, we
created dummy variables to control for the article’s section
(e.g., Section A). We also created indicator variables to
quantify the page in a given section (e.g., A1) where an arti-
cle appeared in print to control for the possibility that
appearing earlier in some sections has a different effect than
appearing earlier in others.
Appearance on the home page. To characterize how much

time an article spent in prominent positions on the home
page, we created variables that indicated where, when, and
for how long every article was featured on the New York
Times home page. The home page layout remained the same
throughout the period of data collection. Articles could
appear in several dozen positions on the home page, so we
aggregated positions into seven general regions based on
locations that likely receive similar amounts of attention
(Figure 1). We included variables indicating the amount of
time an article spent in each of these seven regions as
controls after Winsorization of the top 1% of outliers (to
prevent extreme outliers from exerting undue influence on
our results; for summary statistics, see Tables WA1 and
WA2 in the Web Appendix at www.marketingpower. com/
jmr_ webappendix).
Release timing and author fame. To control for the possi-

bility that articles released at different times of day receive
different amounts of attention, we created controls for the
time of day (6 A.M.–6 P.M. or 6 P.M.–6 A.M. eastern standard
time) when an article first appeared online. We control for
author fame to ensure that our results are not driven by the
tastes of particularly popular writers whose stories may be
more likely to be shared. To quantify author fame, we cap-
ture the number of Google hits returned by a search for each
first author’s full name (as of February 15, 2009). Because

Table 1
ARTICLES THAT SCORED HIGHLY ON DIFFERENT DIMENSIONS

Primary Predictors
Emotionality

•“Redefining Depression as Mere Sadness”
•“When All Else Fails, Blaming the Patient Often Comes Next”

Positivity
•“Wide-Eyed New Arrivals Falling in Love with the City”
•“Tony Award for Philanthropy”

(Low Scoring)
•“Web Rumors Tied to Korean Actress’s Suicide”
•“Germany: Baby Polar Bear’s Feeder Dies”

Awe
•“Rare Treatment Is Reported to Cure AIDS Patient”
•“The Promise and Power of RNA”

Anger
•“What Red Ink? Wall Street Paid Hefty Bonuses”
•“Loan Titans Paid McCain Adviser Nearly $2 Million”

Anxiety
•“For Stocks, Worst Single-Day Drop in Two Decades”
•“Home Prices Seem Far from Bottom”

Sadness
•“Maimed on 9/11, Trying to Be Whole Again”
•“Obama Pays Tribute to His Grandmother After She Dies”

Control Variables
Practical Utility

•“Voter Resources”
•“It Comes in Beige or Black, but You Make It Green” (a story
about being environmentally friendly when disposing of old
computers)

Interest
•“Love, Sex and the Changing Landscape of Infidelity”
•“Teams Prepare for the Courtship of LeBron James”

Surprise
•“Passion for Food Adjusts to Fit Passion for Running” (a story
about a restaurateur who runs marathons)
•“Pecking, but No Order, on Streets of East Harlem” (a story about
chickens in Harlem)

Table 2
PREDICTOR VARIABLE SUMMARY STATISTICS

M SD
Primary Predictor Variables
Emotionalitya 7.43% 1.92%
Positivitya .98% 1.84%
Awea 1.81 .71
Angera 1.47 .51
Anxietya 1.55 .64
Sadnessa 1.31 .41

Other Control Variables
Practical utilitya 1.66 1.01
Interesta 2.71 .85
Surprisea 2.25 .87
Word count 1021.35 668.94
Complexitya 11.08 1.54
Author fame 9.13 2.54
Author female .29 .45
Author male .66 .48
aThese summary statistics pertain to the variable in question before

standardization.

of its skew, we use the logarithm of this variable as a con-
trol in our analyses. We also control for variables that might
both influence transmission and the likelihood that an arti-
cle possesses certain characteristics (e.g., evokes anger).
Writing complexity. We control for how difficult a piece

of writing is to read using the SMOG Complexity Index
(McLaughlin 1969). This widely used index variable essen-
tially measures the grade-level appropriateness of the writ-
ing. Alternate complexity measures yield similar results.
Author gender. Because male and female authors have

different writing styles (Koppel, Argamon, and Shimoni
2002; Milkman, Carmona, and Gleason 2007), we control
for the gender of an article’s first author (male, female, or
unknown due to a missing byline). We classify gender using
a first name mapping list from prior research (Morton,
Zettelmeyer, and Silva-Risso 2003). For names that were
classified as gender neutral or did not appear on this list,
research assistants determined author gender by finding the
authors online.
Article length and day dummies. We also control for an

article’s length in words. Longer articles may be more likely
to go into enough detail to inspire awe or evoke anger but
may simply be more viral because they contain more infor-

mation. Finally, we use day dummies to control for both
competition among articles to make the most e-mailed list
(i.e., other content that came out the same day) as well as
any other time-specific effects (e.g., world events that might
affect article characteristics and reader interest).
Analysis Strategy
Almost all articles that make the most e-mailed list do so

only once (i.e., they do not leave the list and reappear), so
we model list making as a single event (for further discus-
sion, see the Web Appendix at www.marketingpower.com/
jmr_ webappendix). We rely on the following logistic
regression specification:

where makes_itat is a variable that takes a value of 1 when
an article a released online on day t earns a position on the
most e-mailed list and 0 otherwise, and t is an unobserved
day-specific effect. Our primary predictor variables quantify
the extent to which article a published on day t was coded as
positive, emotional, awe inspiring, anger inducing, anxiety
inducing, or sadness inducing. The term Xat is a vector of
the other control variables described previously (see Table
3). We estimate the equation with fixed effects for the day
of an article’s release, clustering standard errors by day of
release. (Results are similar if fixed effects are not included.)
Results
Is positive or negative content more viral? First, we

examine content valence. The results indicate that content is
more likely to become viral the more positive it is (Table 4,
Model 1). Model 2 shows that more affect-laden content,
regardless of valence, is more likely to make the most e-
mailed list, but the returns to increased positivity persist
even controlling for emotionality more generally. From a
different perspective, when we include both the percentage
of positive and negative words in an article as separate pre-
dictors (instead of emotionality and valence), both are posi-
tively associated with making the most e-mailed list. How-
ever, the coefficient on positive words is considerably larger
than that on negative words. This indicates that while more
positive or more negative content is more viral than content
that does not evoke emotion, positive content is more viral
than negative content.
The nature of our data set is particularly useful here

because it enables us to disentangle preferential transmis-
sion from mere base rates (see Godes et al. 2005). For
example, if it were observed that there was more positive
than negative word of mouth overall, it would be unclear
whether this outcome was driven by (1) what people
encounter (e.g., people may come across more positive
events than negative ones) or (2) what people prefer to pass
on (i.e., positive or negative content). Thus, without know-
ing what people could have shared, it is difficult to infer
much about what they prefer to share. Access to the full cor-

+ −

α + β ×
+ β ×
+ β × + β ×
+ β ×
+ β × + ′θ ×















































(1)makes_it 1

1 exp

z-emotionality
z-positivity
z-awe z-anger
z-anxiety
z-sadness X

,at
t 1 at
2 at
3 at 4 at
5 at
6 at at

Table 3
PREDICTOR VARIABLES

Variable Where It Came from
Main Independent Variables
Emotionality Coded through textual analysis

(LIWC)
Positivity Coded through textual analysis

(LIWC)
Awe Manually coded
Anger Manually coded
Anxiety Manually coded
Sadness Manually coded

Content Controls
Practical utility Manually coded
Interest Manually coded
Surprise Manually coded

Other Control Variables
Word count Coded through textual analysis

(LIWC)
Author fame Log of number of hits returned by

Google search of author’s name
Writing complexity SMOG Complexity Index

(McLaughlin 1969)
Author gender List mapping names to genders

(Morton et al. 2003)
Author byline missing Captured by web crawler
Article section Captured by web crawler
Hours spent in different places on Captured by web crawler
the home page

Section of the physical paper Captured by web crawler
(e.g., A)

Page in section in the physical Captured by web crawler
paper (e.g., A1)

Time of day the article appeared Captured by web crawler
Day the article appeared Captured by web crawler
Category of the article (e.g., sports) Captured by web crawler

196 JOURNAL OF MARKETING RESEARCH, APRIL 2012

What Makes Online Content Viral? 197

Figure 1
HOME PAGE LOCATION CLASSIFICATIONS

!”#$%

‘()*+,&

-(./&0/12&

-“33)/&4/5%*./&65.&

6(7(+&8″2%&

9(:&4/5%*./&

0/5.&9(:&
4/5%*./&

6*))/%/3&
;*<=4/5%*./&

&
&

& &

& & &

&
& &
&
&
&
&
& &
& & &
&
& &
&
&
&
&
& &
& & &
&
& &
&
&
&
&
& &
& & &
&
& &
&
&
&
&
& &
& & &
&
& &
&
&
&
&
& &
& & &
&
& &
&
&
&
&
& &
& & &
&
& &
&
&
&
&
& &
& & &
&
& &
&
&

:
&
&

& &
& & &
&
& &
&
&
&
&
& &
& & &
&
& &
&
&
&
&
& &
& & &
&
& &
&
&
&
&
& &
& & &
&
& &
&
&
&
&
& &
& & &
&
& &
&
&
&
&
& &
& & &
&
& &
&
&
&
&
& &
& & &
&
& &
&
&

55/
/

&
&
& &
& & &
&
& &
&
&

%))/
5/4;*<=

&
&
& &
& & &
&
& &
&
&

3&%
*.%5

&
&
& &
& & &
&
& &
&
&
&
&
& &
& & &
&
& &
&
&
&
&
& &
& & &
&
& &
&
&
&
&
& &
& & &
&
& &
&
&
&
&
& &
& & &
&
& &
&
&
&
&
& &
& & &
&
& &
&
&
&
&
& &
& & &
&
& &
&
&
&
&
& &
& & &
&
& &
&
&
&
&
& &
& & &
&
& &
&
&
&
&
& &
& & &
&
& &
&
&
&
&
& &
& & &
&
& &
&
&
&
&
& &
& & &
&
& &
&
&
&
&
& &
& & &
&
& &
&
&
&
&
& &
& & &
&
& &
&
&
&
&
& &
& & &
&
& &
&
&
&
&
& &
& & &
&
& &
&
&
&
&
& &
& & &
&
& &
&
&

Notes: Portions with “X” through them always featured Associated Press and Reuters news stories, videos, blogs, or advertisements rather than articles by
New York Times reporters.

pus of articles published by the New York Times over the
analysis period as well as all content that made the most e-
mailed list enables us to separate these possibilities. Taking
into account all published articles, our results show that an
article is more likely to make the most e-mailed list the
more positive it is.
How do specific emotions affect virality? The relation-

ships between specific emotions and virality suggest that the
role of emotion in transmission is more complex than mere
valence alone (Table 4, Model 3). While more awe-inspiring
(a positive emotion) content is more viral and sadness-
inducing (a negative emotion) content is less viral, some
negative emotions are positively associated with virality.
More anxiety- and anger-inducing stories are both more
likely to make the most e-mailed list. This suggests that
transmission is about more than simply sharing positive
things and avoiding sharing negative ones. Consistent with
our theorizing, content that evokes high-arousal emotions
(i.e., awe, anger, and anxiety), regardless of their valence, is
more viral.
Other factors. These results persist when we control for a

host of other factors (Table 4, Model 4). More notably,
informative (practically useful), interesting, and surprising
articles are more likely to make the New York Times’ most
e-mailed list, but our focal results are significant even after
we control for these content characteristics. Similarly, being

featured for longer in more prominent positions on the New
York Times home page (e.g., the lead story vs. at the bottom
of the page) is positively associated with making the most
e-mailed list, but the relationships between emotional char-
acteristics of content and virality persist even after we con-
trol for this type of “advertising.” This suggests that the
heightened virality of stories that evoke certain emotions is
not simply driven by editors featuring those types of stories,
which could mechanically increase their virality.4 Longer
articles, articles by more famous authors, and articles writ-
ten by women are also more likely to make the most e-
mailed list, but our results are robust to including these fac-
tors as well.
Robustness checks. The results are also robust to control-

ling for an article’s general topic (20 areas classified by the
New York Times, such as science and health; Table 4, Model
5). This indicates that our findings are not merely driven by
certain areas tending to both evoke certain emotions and be
particularly likely to make the most e-mailed list. Rather,

4Furthermore, regressing the various content characteristics on being
featured suggests that topical section (e.g., national news vs. sports), rather
than an integral affect, determines where articles are featured. The results
show that general topical areas (e.g., opinion) are strongly related to
whether and where articles are featured on the home page, while emotional
characteristics are not.

198 JOURNAL OF MARKETING RESEARCH, APRIL 2012

Table 4
AN ARTICLE’S LIKELIHOOD OF MAKING THE NEW YORK TIMES’ MOST E-MAILED LIST AS A FUNCTION OF ITS CONTENT

CHARACTERISTICS

Specific Including Including Section Only Coded
Positivity Emotionality Emotions Controls Dummies Articles
(1) (2) (3) (4) (5) (6)

Emotion Predictors
Positivity .13*** .11*** .17*** .16*** .14*** .23***

(.03) (.03) (.03) (.04) (.04) (.05)
Emotionality — .27*** .26*** .22*** .09* .29***

— (.03) (.03) (.04) (.04) (.06)
Specific Emotions
Awe — — .46*** .34*** .30*** .36***

— — (.05) (.05) (.06) (.06)
Anger — — .44*** .38*** .29** .37***

— — (.06) (.09) (.10) (.10)
Anxiety — — .20*** .24*** .21*** .27***

— — (.05) (.07) (.07) (.07)
Sadness — — –.19*** –.17* –.12† –.16*

— — (.05) (.07) (.07) (.07)
Content Controls
Practical utility — — — .34*** .18** .27***

— — — (.06) (.07) (.06)
Interest — — — .29*** .31*** .27***

— — — (.06) (.07) (.07)
Surprise — — — .16** .24*** .18**

— — — (.06) (.06) (.06)
Home Page Location Control Variables
Top feature — — — .13*** .11*** .11***

— — — (.02) (.02) (.03)
Near top feature — — — .11*** .10*** .12***

— — — (.01) (.01) (.01)
Right column — — — .14*** .10*** .15***

— — — (.01) (.02) (.02)
Middle feature bar — — — .06*** .05*** .06***

— — — (.00) (.01) (.01)
Bulleted subfeature — — — .04** .04** .05*

— — — (.01) (.01) (.02)
More news — — — .01 .06*** –.01

— — — (.01) (.01) (.02)
Bottom list ¥ 10 — — — .06** .11*** .08**

— — — (.02) (.03) (.03)
Other Control Variables
Word count ¥ 10–3 — — — .52*** .71*** .57***

— — — (.11) (.12) (.18)
Complexity — — — .05 .05 .06

— — — (.04) (.04) (.07)
First author fame — — — .17*** .15*** .15***

— — — (.02) (.02) (.03)
Female first author — — — .36*** .33*** .27*

— — — (.08) (.09) (.13)
Uncredited — — — .39 –.56* .50

— — — (.26) (.27) (.37)
Newspaper location and
web timing controls No No No Yes Yes Yes

Article section dummies
(e.g., arts, books) No No No No Yes No

Observations 6956 6956 6956 6956 6956 2566
McFadden’s R2 .00 .04 .07 .28 .36 .32
Log-pseudo-likelihood –3245.85 –3118.45 –3034.17 –2331.37 –2084.85 –904.76
†Significant at the 10% level.
*Significant at 5% level.
**Significant at 1% level.
***Significant at the .1% level.
Notes: The logistic regressions models that appear in this table predict whether an article makes the New York Times’ most emailed list. Successive models

include added control variables, with the exception of Model 6. Model 6 presents our primary regression specification (see Model 4), including only observa-
tions of articles whose content was hand-coded by research assistants. All models include day fixed effects. Models 4–6 include disgust (hand-coded) as a
control because disgust has been linked to transmission in previous research (Heath et al. 2001), and including this control allows for a more conservative test
of our hypotheses. Its effect is never significant, and dropping this control variable does not change any of our results.

What Makes Online Content Viral? 199

this more conservative test of our hypothesis suggests that
the observed relationships between emotion and virality hold
not only across topics but also within them. Even among
opinion or health articles, for example, awe-inspiring arti-
cles are more viral.
Finally, our results remain meaningfully unchanged in

terms of magnitude and significance if we perform a host of
other robustness checks, including analyzing only the 2566
hand-coded articles (Table 4, Model 6), removing day fixed
effects, and using alternate ways of quantifying emotion
(for more robustness checks and analyses using article rank
or time on the most e-mailed list as alternate dependent
measures, see the Web Appendix at www.marketingpower.
com/ jmr_webappendix). These results indicate that the
observed results are not an artifact of the particular regres-
sion specifications we rely on in our primary analyses.
Discussion
Analysis of more than three months of New York Times

articles sheds light on what types of online content become
viral and why. Contributing to the debate on whether posi-
tive or negative content is more likely to be shared, our
results demonstrate that more positive content is more viral.
Importantly, however, our findings also reveal that virality
is driven by more than just valence. Sadness, anger, and
anxiety are all negative emotions, but while sadder content
is less viral, content that evokes more anxiety or anger is
actually more viral. These findings are consistent with our
hypothesis about how arousal shapes social transmission.
Positive and negative emotions characterized by activation
or arousal (i.e., awe, anxiety, and anger) are positively
linked to virality, while emotions characterized by deactiva-
tion (i.e., sadness) are negatively linked to virality.
More broadly, our results suggest that while external

drivers of attention (e.g., being prominently featured) shape
what becomes viral, content characteristics are of similar
importance (see Figure 2). For example, a one-standard-
deviation increase in the amount of anger an article evokes
increases the odds that it will make the most e-mailed list
by 34% (Table 4, Model 4). This increase is equivalent to
spending an additional 2.9 hours as the lead story on the
New York Times website, which is nearly four times the
average number of hours articles spend in that position.
Similarly, a one-standard-deviation increase in awe increases
the odds of making the most e-mailed list by 30%.
These field results are consistent with the notion that acti-

vation drives social transmission. To more directly test the
process behind our specific emotions findings, we turn to
the laboratory.
STUDY 2: HOW HIGH-AROUSAL EMOTIONS AFFECT

TRANSMISSION
Our experiments had three main goals. First, we wanted

to directly test the causal impact of specific emotions on
sharing. The field study illustrates that content that evokes
activating emotions is more likely to be viral, but by manip-
ulating specific emotions in a more controlled setting, we
can more cleanly examine how they affect transmission.
Second, we wanted to test the hypothesized mechanism
behind these effects—namely, whether the arousal induced
by content drives transmission. Third, while the New York
Times provided a broad domain to study transmission, we

wanted to test whether our findings would generalize to
other marketing content.
We asked participants how likely they would be to share

a story about a recent advertising campaign (Study 2a) or
customer service experience (Study 2b) and manipulated
whether the story in question evoked more or less of a spe-
cific emotion (amusement in Study 2a and anger in Study
2b). To test the generalizability of the effects, we examined
how both positive (amusement, Study 2a) and negative
(anger, Study 2b) high-arousal emotions characterized influ-
ence transmission. If arousal increases sharing, content that
evokes more of an activating emotion (amusement or anger)
should be more likely to be shared. Finally, we measured
experienced activation to test whether it drives the effect of
emotion on sharing.
Study2a: Amusement
Participants (N = 49) were randomly assigned to read

either a high- or low-amusement version of a story about a
recent advertising campaign for Jimmy Dean sausages. The
two versions were adapted from prior work (McGraw and
Warren 2010) showing that they differed on how much
humor they evoked (a pretest showed that they did not differ
in how much interest they evoked). In the low-amusement
condition, Jimmy Dean decides to hire a farmer as the new
spokesperson for the company’s line of pork products. In
the high-amusement condition, Jimmy Dean decides to hire
a rabbi (which is funny given that the company makes pork
products and that pork is not considered kosher). After read-
ing about the campaign, participants were asked how likely

Figure 2
PERCENTAGE CHANGE IN FITTED PROBABILITY OF MAKING
THE LIST FOR A ONE-STANDARD-DEVIATION INCREASE
ABOVE THE MEAN IN AN ARTICLE CHARACTERISTIC

% Change in Fitted Probability of Making the List
–20% 20% 40%0%

21%

34%

–16%

30%

13%

18%

25%

14%

30%

20%

Anxiety (+1SD)

Anger (+1SD)

Sadness (+1SD)

Awe (+1SD)

Positivity (+1SD)

Emotionality (+1SD)

Interest (+1SD)

Surprise (+1SD)

Practical Value (+1SD)

Time at top of
home page (+1SD)

200 JOURNAL OF MARKETING RESEARCH, APRIL 2012

they would be to share it with others (1 = “not at all likely,”
and 7 = “extremely likely”).
Participants also rated their level of arousal using three

seven-point scales (“How do you feel right now?” Scales
were anchored at “very passive/very active,” “very mellow/
very fired up,” and “very low energy/very high energy”:  =
82; we adapted this measure from Berger [2011] and aver-
aged the responses to form an activation index).
Results. As we predicted, participants reported they

would be more likely to share the advertising campaign
when it induced more amusement, and this was driven by
the arousal it evoked. First, participants reported that they
would be more likely to share the advertisement if they
were in the high-amusement (M = 3.97) as opposed to low-
amusement condition (M = 2.92; F(1, 47) = 10.89, p < .005). Second, the results were similar for arousal; the high- amusement condition (M = 3.73) evoked more arousal than the low-amusement condition (M = 2.92; F(1, 47) = 5.24, p < .05). Third, as we predicted, this boost in arousal medi- ated the effect of the amusement condition on sharing. Con- dition was linked to arousal (high_amusement = .39, SE = .17; t(47) = 2.29, p < .05); arousal was linked to sharing (activa- tion = .58, SE = .11; t(47) = 5.06, p < .001); and when we included both the amusement condition and arousal in a regression predicting sharing, arousal mediated the effect of amusement on transmission (Sobel z = 2.02, p < .05). Study2b: Anger Participants (N = 45) were randomly assigned to read

either a high- or low-anger version of a story about a (real)
negative customer service experience with United Airlines
(Negroni 2009). We pretested the two versions to ensure
that they evoked different amounts of anger but not other
specific emotions, interest, or positivity in general. In both
conditions, the story described a music group traveling on
United Airlines to begin a week-long-tour of shows in
Nebraska. As they were about to leave, however, they
noticed that the United baggage handlers were mishandling
their guitars. They asked for help from flight attendants, but
by the time they landed, the guitars had been damaged. In
the high-anger condition, the story was titled “United
Smashes Guitars,” and it described how the baggage han-
dlers seemed not to care about the guitars and how United
was unwilling to pay for the damages. In the low-anger con-
dition, the story was titled “United Dents Guitars,” and it
described the baggage handlers as having dropped the gui-
tars but United was willing to help pay for the damages.
After reading the story, participants rated how likely they
would be to share the customer service experience as well
as their arousal using the scales from Study 2a.
Results. As we predicted, participants reported that they

would be more likely to share the customer service experi-
ence when it induced more anger, and this was driven by the
arousal it evoked. First, participants reported being more
likely to share the customer service experience if they were
in the high-anger condition (M = 5.71) as opposed to low-
anger condition (M = 3.37; F(1, 43) = 18.06, p < .001). Sec- ond, the results were similar for arousal; the high-anger con- dition (M = 4.48) evoked more arousal than the low-anger condition (M = 3.00; F(1, 43) = 10.44, p < .005). Third, as in Study 2a, this boost in arousal mediated the effect of con- dition on sharing. Regression analyses show that condition

was linked to arousal (high_anger = .74, SE = .23; t(44) =
3.23, p < .005); arousal was linked to sharing (activation = .65, SE = .17; t(44) = 3.85, p < .001); and when we included both anger condition and arousal in a regression, arousal mediated the effect of anger on transmission (Sobel z = 1.95, p = .05). Discussion The experimental results reinforce the findings from our

archival field study, support our hypothesized process, and
generalize our findings to a broader range of content. First,
consistent with our analysis of the New York Times’ most e-
mailed list, the amount of emotion content evoked influ-
enced transmission. People reported that they would be
more likely to share an advertisement when it evoked more
amusement (Study 2a) and a customer service experience
when it evoked more anger (Study 2b). Second, the results
underscore our hypothesized mechanism: Arousal mediated
the impact of emotion on social transmission. Content that
evokes more anger or amusement is more likely to be
shared, and this is driven by the level of activation it
induces.
STUDY 3: HOW DEACTIVATING EMOTIONS AFFECT

TRANSMISSION
Our final experiment further tests the role of arousal by

examining how deactivating emotions affect transmission.
Studies 2a and 2b show that increasing the amount of high-
arousal emotions boosts social transmission due to the acti-
vation it induces, but if our theory is correct, these effects
should reverse for low-arousal emotions. Content that
evokes more sadness, for example, should be less likely to
be shared because it deactivates rather than activates.
Note that this is a particularly strong test of our theory

because the prediction goes against several alternative
explanations for our findings in Study 2. It could be argued
that evoking more of any specific emotion makes content
better or more compelling, but such an explanation would
suggest that evoking more sadness should increase (rather
than decrease) transmission.
Method
Participants (N = 47) were randomly assigned to read

either a high- or low-sadness version of a news article. We
pretested the two versions to ensure that they evoked differ-
ent amounts of sadness but not other specific emotions,
interest, or positivity in general. In both conditions, the arti-
cle described someone who had to have titanium pins
implanted in her hands and relearn her grip after sustaining
injuries. The difference between conditions was the source
of the injury. In the high-sadness condition, the story was
taken directly from our New York Times data set. It was
titled “Maimed on 9/11: Trying to Be Whole Again,” and it
detailed how someone who worked in the World Trade Cen-
ter sustained an injury during the September 11 attacks. In
the low-sadness condition, the story was titled “Trying to
Be Better Again,” and it detailed how the person sustained
the injury falling down the stairs at her office. After reading
one of these two versions of the story, participants answered
the same sharing and arousal questions as in Study 2.
As we predicted, when the context evoked a deactivating

(i.e., de-arousing) emotion, the effects on transmission were

What Makes Online Content Viral? 201

reversed. First, participants were less likely to share the story
if they were in the high-sadness condition (M = 2.39) as
opposed to the low-sadness condition (M = 3.80; F(1, 46) =
10.78, p < .005). Second, the results were similar for arousal; the high-sadness condition (M = 2.75) evoked less arousal than the low-sadness condition (M = 3.89; F(1, 46) = 10.29, p < .005). Third, as we hypothesized, this decrease in arousal mediated the effect of condition on sharing. Condition was linked to arousal (high_sadness = –.57, SE = .18; t(46) = –3.21, p < .005); arousal was linked to sharing (activation = .67, SE = .15, t(46) = 4.52, p < .001); and when we included both sadness condition and arousal in a regression predict- ing sharing, arousal mediated the effect of sadness on trans- mission (Sobel z = –2.32, p < .05). Discussion The results of Study 3 further underscore the role of

arousal in social transmission. Consistent with the findings
of our field study, when content evoked more of a low-
arousal emotion, it was less likely to be shared. Further-
more, these effects were again driven by arousal. When a
story evoked more sadness, it decreased arousal, which in
turn decreased transmission. The finding that the effect of
specific emotion intensity on transmission reversed when
the emotion was deactivating provides even stronger evi-
dence for our theoretical perspective. While it could be
argued that content evoking more emotion is more interest-
ing or engaging (and, indeed, pretest results suggest that this
is the case in this experiment), these results show that such
increased emotion may actually decrease transmission if the
specific emotion evoked is characterized by deactivation.

GENERAL DISCUSSION
The emergence of social media (e.g., Facebook, Twitter)

has boosted interest in word of mouth and viral marketing.
It is clear that consumers often share online content and that
social transmission influences product adoption and sales,
but less is known about why consumers share content or
why certain content becomes viral. Furthermore, although
diffusion research has examined how certain people (e.g.,
social hubs, influentials) and social network structures
might influence social transmission, but less attention has
been given to how characteristics of content that spread
across social ties might shape collective outcomes.
The current research takes a multimethod approach to

studying virality. By combining a broad analysis of virality
in the field with a series of controlled laboratory experi-
ments, we document characteristics of viral content while
also shedding light on what drives social transmission.
Our findings make several contributions to the existing

literature. First, they inform the ongoing debate about
whether people tend to share positive or negative content.
While common wisdom suggests that people tend to pass
along negative news more than positive news, our results
indicate that positive news is actually more viral. Further-
more, by examining the full corpus of New York Times con-
tent (i.e., all articles available), we determine that positive
content is more likely to be highly shared, even after we
control for how frequently it occurs.
Second, our results illustrate that the relationship between

emotion and virality is more complex than valence alone
and that arousal drives social transmission. Consistent with

our theorizing, online content that evoked high-arousal
emotions was more viral, regardless of whether those emo-
tions were of a positive (i.e., awe) or negative (i.e., anger or
anxiety) nature. Online content that evoked more of a deac-
tivating emotion (i.e., sadness), however, was actually less
likely to be viral. Experimentally manipulating specific
emotions in a controlled environment confirms the hypothe-
sized causal relationship between activation and social
transmission. When marketing content evoked more of spe-
cific emotions characterized by arousal (i.e., amusement in
Study 2a or anger in Study 2b), it was more likely to be
shared, but when it evoked specific emotion characterized
by deactivation (i.e., sadness in Study 3), it was less likely to
be shared. In addition, these effects were mediated by arousal,
further underscoring its impact on social transmission.
Demonstrating these relationships in both the laboratory

and the field, as well as across a large and diverse body of
content, underscores their generality. Furthermore, although
not a focus of our analysis, our field study also adds to the
literature by demonstrating that more practically useful,
interesting, and surprising content is more viral. Finally, the
naturalistic setting allows us to measure the relative impor-
tance of content characteristics and external drivers of atten-
tion in shaping virality. While being featured prominently,
for example, increases the likelihood that content will be
highly shared, our results suggest that content characteris-
tics are of similar importance.
Theoretical Implications
This research links psychological and sociological

approaches to studying diffusion. Prior research has mod-
eled product adoption (Bass 1969) and examined how social
networks shape diffusion and sales (Van den Bulte and
Wuyts 2007). However, macrolevel collective outcomes
(such as what becomes viral) also depend on microlevel
individual decisions about what to share. Consequently,
when trying to understand collective outcomes, it is impor-
tant to consider the underlying individual-level psychologi-
cal processes that drive social transmission (Berger 2011;
Berger and Schwartz 2011). Along these lines, this work
suggests that the emotion (and activation) that content
evokes helps determine which cultural items succeed in the
marketplace of ideas.
Our findings also suggest that social transmission is

about more than just value exchange or self-presentation
(see also Berger and Schwartz 2011). Consistent with the
notion that people share to entertain others, surprising and
interesting content is highly viral. Similarly, consistent with
the notion that people share to inform others or boost their
mood, practically useful and positive content is more viral.
These effects are all consistent with the idea that people
may share content to help others, generate reciprocity, or
boost their reputation (e.g., show they know entertaining or
useful things). Even after we control for these effects, how-
ever, we find that highly arousing content (e.g., anxiety
evoking, anger evoking) is more likely to make the most e-
mailed list. Such content does not clearly produce immedi-
ate economic value in the traditional sense or even neces-
sarily reflect favorably on the self. This suggests that social
transmission may be less about motivation and more about
the transmitter’s internal states.

202 JOURNAL OF MARKETING RESEARCH, APRIL 2012

It is also worthwhile to consider these findings in relation
to literature on characteristics of effective advertising. Just
as certain characteristics of advertisements may make them
more effective, certain characteristics of content may make
it more likely to be shared. While there is likely some over-
lap in these factors (e.g., creative advertisements are more
effective [Goldenberg, Mazursky, and Solomon 1999] and
are likely shared more), there may also be some important
differences. For example, while negative emotions may hurt
brand and product attitudes (Edell and Burke 1987), we
have shown that some negative emotions can actually
increase social transmission.
Directions for Further Research
Future work might examine how audience size moderates

what people share. People often e-mail online content to a
particular friend or two, but in other cases they may broad-
cast content to a much larger audience (e.g., tweeting, blog-
ging, posting it on their Facebook wall). Although the for-
mer (i.e., narrowcasting) can involve niche information
(e.g., sending an article about rowing to a friend who likes
crew), broadcasting likely requires posting content that has
broader appeal. It also seems likely that whereas narrow-
casting is recipient focused (i.e., what a recipient would
enjoy), broadcasting is self focused (i.e., what someone wants
to say about him- or herself or show others). Consequently,
self-presentation motives, identity signaling (e.g., Berger
and Heath 2007), or affiliation goals may play a stronger
role in shaping what people share with larger audiences.
Although our data do not allow us to speak to this issue

in great detail, we were able to investigate the link between
article characteristics and blogging. Halfway into our data
collection, we built a supplementary web crawler to capture
the New York Times’ list of the 25 articles that had appeared
in the most blogs over the previous 24 hours. Analysis sug-
gests that similar factors drive both virality and blogging:
More emotional, positive, interesting, and anger-inducing
and fewer sadness-inducing stories are likely to make the
most blogged list. Notably, the effect of practical utility
reverses: Although a practically useful story is more likely
to make the most e-mailed list, practically useful content is
marginally less likely to be blogged about. This may be due
in part to the nature of blogs as commentary. While movie
reviews, technology perspectives, and recipes all contain
useful information, they are already commentary, and thus
there may not be much added value from a blogger con-
tributing his or her spin on the issue.
Further research might also examine how the effects

observed here are moderated by situational factors. Given
that the weather can affect people’s moods (Keller et al.
2005), for example, it may affect the type of content that is
shared. People might be more likely to share positive stories
on overcast days, for example, to make others feel happier.
Other cues in the environment might also shape social trans-
mission by making certain topics more accessible (Berger
and Fitzsimons 2008; Berger and Schwartz 2011; Nedun-
gadi 1990). When the World Series is going on, for exam-
ple, people may be more likely to share a sports story
because that topic has been primed.
These findings also raise broader questions, such as how

much of social transmission is driven by the sender versus
the receiver and how much of it is motivated versus unmoti-

vated. While intuition might suggest that much of transmis-
sion is motivated (i.e., wanting to look good to others) and
based on the receiver and what he or she would find of value,
the current results highlight the important role of the sender’s
internal states in whether something is shared. That said, a
deeper understanding of these issues requires further research.
Marketing Implications
These findings also have important marketing implica-

tions. Considering the specific emotions content evokes
should help companies maximize revenue when placing
advertisements and should help online content providers
when pricing access to content (e.g., potentially charging
more for content that is more likely to be shared). It might
also be useful to feature or design content that evokes acti-
vating emotions because such content is likely to be shared
(thus increasing page views).
Our findings also shed light on how to design successful

viral marketing campaigns and craft contagious content.
While marketers often produce content that paints their
product in a positive light, our results suggest that content
will be more likely to be shared if it evokes high-arousal
emotions. Advertisements that make consumers content or
relaxed, for example, will not be as viral as those that amuse
them. Furthermore, while some marketers might shy away
from advertisements that evoke negative emotions, our
results suggest that negative emotion can actually increase
transmission if it is characterized by activation. BMW, for
example, created a series of short online films called “The
Hire” that they hoped would go viral and which included
car chases and story lines that often evoked anxiety (with
such titles as “Ambush” and “Hostage”). While one might
be concerned that negative emotion would hurt the brand,
our results suggest that it should increase transmission
because anxiety induces arousal. (Incidentally, “The Hire”
was highly successful, generating millions of views). Fol-
lowing this line of reasoning, public health information
should be more likely to be passed on if it is framed to
evoke anger or anxiety rather than sadness.
Similar points apply to managing online consumer senti-

ment. While some consumer-generated content (e.g.,
reviews, blog posts) is positive, much is negative and can
build into consumer backlashes if it is not carefully man-
aged. Mothers offended by a Motrin ad campaign, for exam-
ple, banded together and began posting negative YouTube
videos and tweets (Petrecca 2008). Although it is impossi-
ble to address all negative sentiment, our results indicate
that certain types of negativity may be more important to
address because they are more likely to be shared. Customer
experiences that evoke anxiety or anger, for example,
should be more likely to be shared than those that evoke
sadness (and textual analysis can be used to distinguish dif-
ferent types of posts). Consequently, it may be more impor-
tant to rectify experiences that make consumers anxious
rather than disappointed.
In conclusion, this research illuminates how content char-

acteristics shape whether it becomes viral. When attempting
to generate word of mouth, marketers often try targeting
“influentials,” or opinion leaders (i.e., some small set of
special people who, whether through having more social
ties or being more persuasive, theoretically have more influ-
ence than others). Although this approach is pervasive,

What Makes Online Content Viral? 203

recent research has cast doubt on its value (Bakshy et al.
2011; Watts 2007) and suggests that it is far from cost effec-
tive. Rather than targeting “special” people, the current
research suggests that it may be more beneficial to focus on
crafting contagious content. By considering how psycho-
logical processes shape social transmission, it is possible to
gain deeper insight into collective outcomes, such as what
becomes viral.

REFERENCES
Allsop, Dee T., Bryce R. Bassett, and James A. Hoskins (2007),
“Word-of-Mouth Research: Principles and Applications,” Jour-
nal of Advertising Research, 47 (4), 388–411.

Anderson, Eugene W. (1998), “Customer Satisfaction and Word-
of-Mouth,” Journal of Service Research, 1 (1), 5–17.

Asch, Solomon E. (1956), “Studies of Independence and Conform-
ity: A Minority of One Against a Unanimous Majority,” Psycho-
logical Monographs, 70 (416), 1–70.

Bakshy, Eytan, Jake M. Hofman, Winter A. Mason, and Duncan J.
Watts (2011), “Everyone’s an Influencer: Quantifying Influence
on Twitter,” Proceedings of the 4th International Conference on
Web Search and Data Mining, Hong Kong, (February 9–12),
65–74.

Barrett, Lisa Feldman and James A. Russell (1998), “Indepen-
dence and Bipolarity in the Structure of Current Affect,” Jour-
nal of Personality and Social Psychology, 74 (4), 967–84.

Bass, Frank (1969), “A New Product Growth Model for Consumer
Durables,” Management Science, 15 (5), 215–27.

Berger, Jonah (2011), “Arousal Increases Social Transmission of
Information,” Psychological Science, 22 (7), 891–93.

——— and Gráinne M. Fitzsimons (2008), “Dogs on the Street,
Pumas on Your Feet: How Cues in the Environment Influence
Product Evaluation and Choice,” Journal of Marketing
Research, 45 (February), 1–14.

——— and Chip Heath (2007), “Where Consumers Diverge from
Others: Identity-Signaling and Product Domains,” Journal of
Consumer Research, 34 (2), 121–34.

——— and Eric Schwartz (2011), “What Drives Immediate and
Ongoing Word-of-Mouth?” Journal of Marketing Research, 48
(October), 869–80.

Brooks, Alison Wood and Maurice E. Schweitzer (2011), “Can
Nervous Nelly Negotiate? How Anxiety Causes Negotiators to
Make Low First Offers, Exit Early, and Earn Less Profit,” Orga-
nizational Behavior and Human Decision Processes, 115 (1),
43–54.

Cashmore, Pete (2009), “YouTube: Why Do We Watch?” CNN.
com, (December 17), (accessed October 14, 2011), [available at
http://www.cnn.com/2009/TECH/12/17/cashmore. youtube/
index. html].

Chevalier, Judith A. and Dina Mayzlin (2006), “The Effect of
Word-of-Mouth on Sales: Online Book Reviews,” Journal of
Marketing Research, 43 (August), 345–54.

Cohen, Jacob and Patricia Cohen (1983), Applied Multiple Regres-
sion/Correlation Analysis for the Behavioral Sciences, 2d ed.
Hillsdale, NJ: Lawrence Erlbaum Associates.

Edell, Julie A. and Marian C. Burke (1987), “The Power of Feel-
ings in Understanding Advertising Effects,” Journal of Con-
sumer Research, 14 (December), 421–33.

Ekman, P., W.V. Friesen, and P. Ellsworth (1982), “What Emotion
Categories or Dimensions Can Observers Judge from Facial
Behavior?” in Emotion in the Human Face, P. Ekman, ed. New
York: Cambridge University Press, 39–55.

Fehr, Ernst, Georg Kirchsteiger, and Arno Riedl (1998), “Gift
Exchange and Reciprocity in Competitive Experimental Mar-
kets,” European Economic Review, 42 (1), 1–34.

Festinger, Leon, Henry W. Riecken, and Stanley Schachter (1956),
When Prophecy Fails. New York: Harper and Row.

Gaertner, Samuel L. and John F. Dovidio (1977), “The Subtlety of
White Racism, Arousal, and Helping Behavior,” Journal of Per-
sonality and Social Psychology, 35 (10), 691–707.

Godes, David and Dina Mayzlin (2004), “Using Online Conversa-
tions to Study Word-of-Mouth Communication,” Marketing Sci-
ence, 23 (4), 545–60.

——— and ——— (2009), “Firm-Created Word-of-Mouth Commu-
nication: Evidence from a Field Test,” Marketing Science, 28
(4), 721–39.

———, ———, Yubo Chen, Sanjiv Das, Chrysanthos Dellarocas,
Bruce Pfeiffer, et al. (2005), “The Firm’s Management of Social
Interactions,” Marketing Letters, 16 (3/4), 415–28.

Goldenberg, Jacob, David Mazursky, and Sorin Solomon (1999),
“Creativity Templates: Towards Identifying the Fundamental
Schemes of Quality Advertisements,” Marketing Science, 18
(3), 333–51.

———, Han Sangman, Donald R. Lehmann, and Jae W. Hong
(2009), “The Role of Hubs in the Adoption Process,” Journal of
Marketing, 73 (March), 1–13.

Goodman, J. (1999), “Basic Facts on Customer Complaint Behav-
ior and the Impact of Service on the Bottom Line,” (accessed
November 10, 2011), [available at http://www.e-satisfy.com/
pdf/ basicfacts. pdf].

Harris, Jacob (2010), “How Often Is the Times Tweeted,” New York
Times Open Blog, (April 15), [available at http://open. blogs.
nytimes.com/2010/04/15/how-often-is-the-times-tweeted/].

Heath, Chip, Chris Bell, and Emily Sternberg (2001), “Emotional
Selection in Memes: The Case of Urban Legends,” Journal of
Personality and Social Psychology, 81 (6), 1028–41.

Heilman, Kenneth M. (1997), “The Neurobiology of Emotional
Experience,” Journal of Neuropsychiatry and Clinical Neuro-
science, 9 (3), 439–48.

Homans, George C. (1958), “Social Behavior as Exchange,”
American Journal of Sociology, 63 (6), 597–606.

Katz, Elihu and Paul Felix Lazarsfeld (1955), Personal Influence:
The Part Played by People in the Flow of Mass Communication.
Glencoe, IL: The Free Press.

Keller, Matthew C., Barbara L. Fredrickson, Oscar Ybarra,
Stephane Cote, Kareem Johnson, Joe Mikels, et al. (2005), “A
Warm Heart and a Clear Head: The Contingent Effects of
Weather on Mood and Cognition,” Psychological Science, 16
(9), 724–31.

Keltner, Dacher and Jon Haidt (2003), “Approaching Awe: A
Moral, Spiritual, and Aesthetic Emotion,” Cognition and Emo-
tion, 17 (2), 297–314.

——— and Jennifer S. Lerner (2010), “Emotion,” in The Handbook
of Social Psychology, 5th ed., D. Gilbert, S. Fiske, and G. Lind-
sey, eds. New York: McGraw-Hill.

Koppel, Moshe, Shlomo Argamon, and Anat Rachel Shimoni
(2002), “Automatically Categorizing Written Texts by Author
Gender,” Literary and Linguistic Computing, 17 (4), 401–412.

McGraw, A. Peter and Caleb Warren (2010), “Benign Violations:
Making Immoral Behavior Funny,” Psychological Science, 21
(8), 1141–49.

McLaughlin, G. Harry (1969), “SMOG Grading: A New Readabil-
ity Formula,” Journal of Reading, 12 (8), 639–46.

Milkman, Katherine L., Rene Carmona, and William Gleason
(2007), “A Statistical Analysis of Editorial Influence and
Author-Character Similarities in 1990s New Yorker Fiction,”
Journal of Literary and Linguistic Computing, 22 (3), 305–328.

Morton, Fiona Scott, Florian Zettelmeyer, and Jorge Silva-Risso
(2003), “Consumer Information and Discrimination: Does the
Internet Affect the Pricing of New Cars to Women and Minori-
ties?” Quantitative Marketing and Economics, 1 (1), 65–92.

Nedungadi, Prakash (1990), “Recall and Consumer Consideration
Sets: Influencing Choice Without Altering Brand Evaluations,”
Journal of Consumer Research, 17 (3), 263–76.

204 JOURNAL OF MARKETING RESEARCH, APRIL 2012

Negroni, Christine (2009), “With Video, a Traveler Fights Back,”
The New York Times, (October 28).

Pang, Bo and Lillian Lee (2008), “Opinion Mining and Sentiment
Analysis,” Foundations and Trends in Information Retrieval, 2
(1/2), 1–135.

Pennebaker, James W., Roger J. Booth, and Martha E. Francis
(2007), LIWC2007: Linguistic Inquiry and Word Count, (accessed
October 14, 2011), [available at http://www.liwc.net/].

Peters, Kim and Yoshihasa Kashima (2007), “From Social Talk to
Social Action: Shaping the Social Triad with Emotion Sharing,”
Journal of Personality and Social Psychology, 93 (5), 780–97.

Petrecca, Laura (2008), “Offended Moms Get Tweet Revenge over
Motrin Ads,” USA Today, (November 19), (accessed October
14, 2011), [available at http://www.usatoday.com/tech/products/
2008-11-18-motrin-ads-twitter_N.htm].

Rime, Bernard, Batja Mesquita, Pierre Philippot, and Stefano Boca
(1991), “Beyond the Emotional Event: Six Studies on the Social
Sharing of Emotion,” Cognition and Emotion, 5 (September–
November), 435–65.

Smith, C.A. and P.C. Ellsworth (1985), “Patterns of Cognitive
Appraisal in Emotion,” Journal of Personality and Social Psy-
chology, 48 (4), 813–38.

Van den Bulte, Christophe and Stefan Wuyts (2007), Social Net-
works and Marketing. Cambridge, MA: Marketing Science
Institute.

Watts, Duncan J. (2007), “Challenging the Influentials Hypothe-
sis,” WOMMA Measuring Word of Mouth, 3, 201–211.

Wojnicki, Andrea C. and Dave Godes (2008), “Word-of-Mouth as
Self-Enhancement,” working paper, University of Toronto.

What Makes Online Content Viral? 205

Ap
pe
nd
ix

C
O
R
R
EL
AT
IO
N
S
BE
TW

EE
N
P
R
ED

IC
TO

R
V
AR

IA
BL
ES

W
or
d

Ne
ar

Bu
lle
ted

M
id
dl
e

Em
ot
io
n-

Pr
ac
tic
al

Co
un
t
Co
mp
lex
–

Au
th
or

To
p

Ri
gh
t

Su
b-

M
or
e

Fe
at
ur
e

al
ity

Po
sit
ivi
ty

Aw
e

An
ge
r

An
xie
ty

Sa
dn
es
s

Ut
ili
ty

In
ter
es
t

Su
rp
ris
e

¥
10
–3

ity
Fa
me

Fe
ma
le

M
iss
in
g

Fe
at
ur
e
Fe
at
ur
e
Co
lu
mn

fea
tu
re

Ne
ws

Ba
r

Em
ot
io
na
lit
y

(1
.00

Po
sit
iv
ity

(.0
4*

(1
.00
Aw
e

–.0
2

(.0
2

(1
.00
An
ge
r
(.0
4*

–.1
6*

–.2
1*

(1
.00

An
xi
ety

(.0
3*

–.1
8*

–.1
1*

(.5
0*

(1
.00
Sa
dn
es
s

(.0
0

–.1
8*

(.0
8*

(.4
2*

(.4
5*

(1
.00

Pr
ac
tic
al
ut
ili
ty

(.0
6*

(.0
4*
–.1
1*

–.1
2*

(.0
7*

–.0
5*

(1
.00
In
ter
es
t

(.0
54
*

(.0
7*

(.2
6*

–.1
3*

–.2
4*

–.1
9*

–.0
6*

(1
.00
Su
rp
ris
e

–.1
0*

–.0
4*

(.2
4*

–.0
1

(.0
0

(.0
5*

–.0
5*

(.1
8*

(1
.00

W
or
d c
ou
nt
¥
10
–3

(.0
6*

.05
*

(.0
4*
(.0
2
(.0
0
(.0
0
–.0
1
(.0
6*

(.0
2*

(1
.00

Co
m
pl
ex
ity

(.0
5*
–.0
5*
–.0
4*

(.1
0*

(.1
3*

(.0
5*

(.0
1

–.1
1*
(.0
4*
–.0
6*
(1
.00

Au
th
or
fa
m
e

–.0
9*

–.0
3*

(.0
6*
(.0
1
(.0
3*
(.0
1
–.0
2
(.0
0
(.0
2
(.0
1
(.0
1
(1
.00

Au
th
or
fe
m
ale

–.0
7*

(.0
6*
(.0
1
–.0
3*
(.0
0
(.0
0
(.0
5*
–.0
1
(.0
7*
(.0
0

–.0
2*

(.0
0
(1
.00
M
iss
in
g

(.2
1*

(.0
3*
–.0
6*
(.0
3*
–.0
2
(.0
0
(.0
1
(.0
2
–.0
9*
–.0
1
(.0
2*

–.7
1*

–.1
5*

(1
.00

To
p f
ea
tu
re

(.0
1
–.0
2
–.0
3*
(.0
6*
(.0
6*
(.0
5*
(.0
2
–.0
3*
–.0
2*

(.2
8*

(.0
1
(.0
0
–.0
2
(.0
1
(1
.00

Ne
ar
to
p f
ea
tu
re

–.0
1
–.0
6*
–.0
2

(.1
5*

(.0
7*
(.0
7*
–.0
3*
–.0
5*
(.0
1

(.2
7*

(.0
6*
(.0
6*
–.0
1
–.0
5*
(.2
7*
(1
.00

Ri
gh
t c
ol
um
n

(.1
6*

(.0
5*
(.0
4*
(.0
0
–.0
2
–.0
2
(.0
5*
(.0
6*
–.0
2*
(.0
5*
–.0
1
–.0
3*
–.0
2
(.1
6*
(.0
2
–.0
4*
(1
.00

Bu
lle
ted
su
bf
ea
tu
re

(.0
0
–.0
2
–.0
5*

(.0
9*

(.0
8*
(.0
6*
(.0
4*
–.0
5*
–.0
4*
(.0
7*
(.0
3*
(.0
3*
(.0
1
–.0
4*

(.1
2*

(.1
2*
–.0
3*
(1
.00

M
or
e n
ew
s

–.0
8*

–.1
1*
–.0
1
(.0
7*
(.0
6*
(.0
6*
–.0
8*
–.0
4*
(.0
7*
–.0
2
(.0
9*
(.0
5*
–.0
1
–.0
7*
(.0
1
(.1
0*
–.0
6*
–.0
5*
(1
.00

M
id
dl
e f
ea
tu
re
ba
r

(.1
1*

(.1
0*

.06
*

–.0
6*
–.0
6*
–.0
5*
(.0
0
(.1
0*
(.0
4*
(.1
6*
–.0
6*
–.1
3*
(.0
0
(.1
3*
(.0
2
–.0
5*
(.0
7*
–.0
4*
–.0
8*
(1
.00

Bo
tto
m
li
st

(.0
3*
(.1
5*

.07
*

–.1
1*
–.0
9*
–.0
6*
(.0
6*
(.0
9*
(.0
4*

(.2
9*

–.0
4*
–.0
6*
(.0
5*
(.0
0
(.0
4*
–.0
5*
(.1
0*
(.0
0
–.0
9*
(.1
3*

*S
ig
ni
fic
an
t a
t t
he
5%

le
ve
l.

https://doi.org/10.1177/2056305119847516

Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-
NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction

and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages
(https://us.sagepub.com/en-us/nam/open-access-at-sage).

Social Media + Society
July-September 2019: 1 –13
© The Author(s) 2019
Article reuse guidelines:
sagepub.com/journals-permissions
DOI: 10.1177/2056305119847516
journals.sagepub.com/home/sms

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 + 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: itai@uga.edu

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

https://uk.sagepub.com/en-gb/journals-permissions

https://journals.sagepub.com/home/sms

mailto:itai@uga.edu

http://crossmark.crossref.org/dialog/?doi=10.1177%2F2056305119847516&domain=pdf&date_stamp=2019-07-21

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 enhancement 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 impact
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, Anderson,
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

4 Social Media + Society

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

Himelboim and Golan 5

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

6 Social Media + Society

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

Himelboim and Golan 7

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 advertisement 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/gFDXwy7F31

8 Social Media + Society

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.

Himelboim and Golan 9

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.

10 Social Media + Society

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 understanding 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 network
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

Himelboim and Golan 11

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

References

Abidin, C. (2016). “Aren’t these just young, rich women doing
vain things online?”: Influencer selfies as subversive frivolity.
Social Media+ Society, 2(2), 2056305116641342.

AdAge.com. (2017). Heineken’s “social experiment” racks up
views. Retrieved from http://adage.com/article/the-viral-video
-chart/viral-video-chart-5-1-17/308885/

Alhabash, S., Baek, J. H., Cunningham, C., & Hagerstrom, A.
(2015). To comment or not to comment? How virality, arousal

level, and commenting behavior on YouTube videos affect
civic behavioral intentions. Computers in Human Behavior,
51, 520–531.

Alhabash, S., & McAlister, A. R. (2015). Redefining virality in
less broad strokes: Predicting viral behavioral intentions from
motivations and uses of Facebook and Twitter. New Media &
Society, 17, 1317–1339.

Alhabash, S., McAlister, A. R., Hagerstrom, A., Quilliam, E. T.,
Rifon, N. J., & Richards, J. I. (2013). Between likes and shares:
Effects of emotional appeal and virality on the persuasiveness
of anticyberbullying messages on Facebook. Cyberpsychology,
Behavior, and Social Networking, 16, 175–182.

Al-Sa’afin, A. (2017). Heineken create the anti-Pepsi advert.
Newshub.co.nz. Retrieved from http://www.newshub.co.nz
/home/entertainment/2017/04/heineken-create-the-anti-pepsi
-advert-aziz-al-sa-afin.html

Araujo, T., Neijens, P., & Vliegenthart, R. (2017). Getting the word
out on Twitter: The role of influentials, information brokers and
strong ties in building word-of-mouth for brands. International
Journal of Advertising, 36, 496–513.

Audrezet, A., De Kerviler, G., & Moulard, J. G. (2018). Authenticity
under threat: When social media influencers need to go beyond
self-presentation. Journal of Business Research. Advance
online publication. doi:10.1016/j.jbusres.2018.07.008

Bakshy, E., Hofman, J. M., Mason, W. A., & Watts, D. J. (2011,
February 9-12). Everyone’s an influencer: Quantifying influ-
ence on Twitter. In Proceedings of the fourth ACM interna-
tional conference on web search and data mining (pp. 65–74).
New York, NY: ACM.

Baños, R. A., Borge-Holthoefer, J., & Moreno, Y. (2013). The role
of hidden influentials in the diffusion of online information
cascades. EPJ Data Science, 2, 6.

Brown, M. R., Bhadury, R. K., & Pope, N. K. L. (2010). The impact
of comedic violence on viral advertising effectiveness. Journal
of Advertising, 39, 49–66.

Brown, D. & Hayes, N. (2008). Influencer Marketing: Who Really
Influences Your Customers? Taylor & Francis.

Burt, R. S. (1992). Structural holes: The social structure of com-
petition. Cambridge, MA: Harvard University Press.

Burt, R. S. (2001). Social capital: Theory and research
(N. L. Karen, S. Cook, & R. S. Burt, Eds.). New York, NY:
Aldine de Gruyter.

Burt, R. S. (2005). Brokerage and closure: An introduction to
social capital. Oxford, UK: Oxford University Press.

Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, K. P. (2010).
Measuring user influence in Twitter: The million follower fal-
lacy. Paper presented at Proceedings of the fourth international
AAAI Conference on Weblogs and Social Media. Retrieved
from http://twitter.mpi-sws.org/icwsm2010_fallacy

Cho, S., Huh, J., & Faber, R. J. (2014). The influence of sender trust
and advertiser trust on multistage effects of viral advertising.
Journal of Advertising, 43, 100–114.

Chu, S. C. (2011). Viral advertising in social media: Participation
in Facebook groups and responses among college-aged users.
Journal of Interactive Advertising, 12, 30–43.

Daniel, E. S., Crawford, J. E. C., & Westerman, D. K. (2018). The
influence of social media influencers: Understanding online
vaping communities and parasocial interaction through the lens
of Taylor’s six-segment strategy wheel. Journal of Interactive
Advertising, 18, 96–109.

https://orcid.org/0000-0001-7981-5613

http://adage.com/article/the-viral-video-chart/viral-video-chart-5-1-17/308885/

http://www.newshub.co.nz/home/entertainment/2017/04/heineken-create-the-anti-pepsi-advert-aziz-al-sa-afin.html

http://twitter.mpi-sws.org/icwsm2010_fallacy

12 Social Media + Society

De Veirman, M., Cauberghe, V., & Hudders, L. (2017). Marketing
through Instagram influencers: The impact of number of fol-
lowers and product divergence on brand attitude. International
Journal of Advertising, 36, 798–828.

Evans, N. J., Phua, J., Lim, J., & Jun, H. (2017). Disclosing
Instagram influencer advertising: The effects of disclosure
language on advertising recognition, attitudes, and behavioral
intent. Journal of Interactive Advertising, 17, 138–149.

Eckler, P., & Bolls, P. (2011). Spreading the virus: Emotional tone
of viral advertising and its effect on forwarding intentions and
attitudes. Journal of Interactive Advertising, 11(2), 1–11.

Freberg, K., Graham, K., McGaughey, K., & Freberg, L. A. (2011).
Who are the social media influencers? A study of public per-
ceptions of personality. Public Relations Review, 37, 90–92.

Goel, S., Anderson, A., Hofman, J., & Watts, D. J. (2015). The
structural virality of online diffusion. Management Science, 62,
180–196.

Golan, G. J., & Zaidner, L. (2008). Creative strategies in viral adver-
tising: An application of Taylor’s six-segment message strat-
egy wheel. Journal of Computer-Mediated Communication,
13, 959–972.

Goldenberg, J., Libai, B., & Muller, E. (2001). Talk of the network:
A complex systems look at the underlying process of word-of
mouth. Marketing Letters, 12, 211–223.

Hansen, D. L., Shneiderman, B., & Smith, M. A. (2011). Analyzing
social media networks with NodeXL: Insights from a connected
world. Burlington, MA: Morgan Kaufmann.

Hayes, J. L., & King, K. W. (2014). The social exchange of viral
ads: Referral and coreferral of ads among college students.
Journal of Interactive Advertising, 14, 98–109.

Hayes, J. L., King, K. W., & Ramirez, A. (2016). Brands, friends,
& viral advertising: A social exchange perspective on the ad
referral processes. Journal of Interactive Marketing, 36, 31–45.

Hayes, N. (2008). Influencer Marketing: Who Really Influences
Your Customers? Taylor & Francis.

Hemsley, J. (2016). Studying the viral growth of a connective action
network using information event signatures. First Monday,
21(8). Retrieved from https://firstmonday.org/ojs/index.php/
fm/article/view/6650/5598

Himelboim, I., Golan, G. J., Moon, B. B., & Suto, R. J. (2014). A
social networks approach to public relations on Twitter: Social
mediators and mediated public relations. Journal of Public
Relations Research, 26, 359–379.

Hinz, O., Skiera, B., Barrot, C., & Becker, J. U. (2011). Seeding
strategies for viral marketing: An empirical comparison.
Journal of Marketing, 75, 55–71.

Horton, D., & Wohl, R. (1956). Mass communication and para-
social interaction: Observations on intimacy at a distance.
Psychiatry, 19, 215–229.

Hsieh, J. K., Hsieh, Y. C., & Tang, Y. C. (2012). Exploring the
disseminating behaviors of eWOM marketing: Persuasion in
online video. Electronic Commerce Research, 12, 201–224.

Huang, J., Su, S., Zhou, L., & Liu, X. (2013). Attitude toward
the viral ad: Expanding traditional advertising models to
interactive advertising. Journal of Interactive Marketing, 27,
36–46.

Isa, D., & Himelboim, I. (2018). A social networks approach to
online social movements: Social mediators and mediated con-
tent in #FreeAJStaff Twitter network. Social Media + Society,
4(1), 2056305118760807.

Jin, S. A. A., & Phua, J. (2014). Following celebrities’ tweets about
brands: The impact of twitter-based electronic word-of-mouth
on consumers’ source credibility perception, buying intention,
and social identification with celebrities. Journal of Advertising,
43(2), 181–195.

Kaplan, A. M., & Haenlein, M. (2011). Two hearts in three-quarter
time: How to waltz the social media/viral marketing dance.
Business Horizons, 54, 253–263.

Keller, K. L. (2009). Building strong brands in a modern mar-
keting communications environment. Journal of Marketing
Communications, 15, 139–155.

Ketelaar, P. E., Janssen, L., Vergeer, M., van Reijmersdal, E. A.,
Crutzen, R., & van’t Riet, J. (2016). The success of viral ads:
Social and attitudinal predictors of consumer pass-on behav-
ior on social network sites. Journal of Business Research, 69,
2603–2613.

Klimmt, C., Hartmann, T., & Schramm, H. (2006). Parasocial inter-
actions and relationships. In J. Bryant & P. Vorderer (Eds.),
Psychology of entertainment (pp. 291–313). Mahwah, NJ:
Lawrence Erlbaum.

Kwak, H., Lee, C., Park, H., & Moon, S. (2010). What is Twitter, a
social network or a news media? Paper presented at Proceedings
of the 19th International Conference on World Wide Web.
Retrieved from https://dl.acm.org/citation.cfm?id=1772751

Lee, M. K., Yoon, H. Y., Smith, M., Park, H. J., & Park, H. W.
(2017). Mapping a Twitter scholarly communication network:
A case of the association of internet researchers’ conference.
Scientometrics, 112, 767–797.

Lou, C., & Yuan, S. (2018). Influencer marketing: How message
value and credibility affect consumer trust of branded con-
tent on social media. Journal of Interactive Advertising, 19,
58–73.

Miles, C. (2014). The rhetoric of managed contagion: Metaphor and
agency in the discourse of viral marketing. Marketing Theory,
14, 3–18.

Newman, M. (2000). The power of design. Nature, 405, 412–413.
Nielsen, J. (2006). Participation inequality: Encouraging more

users to contribute. Retrieved from http://www.useit.com
/alertbox/participation_inequality.html

Okazaki, S., Katsukura, A., & Nishiyama, M. (2007). How mobile
advertising works: The role of trust in improving attitudes and
recall. Journal of Advertising Research, 47, 165–178.

Pathak, S. (2017, August 10). Report: Brands are falling for
fake Instagram influencers. Digiday.com. Retrieved from
https://digiday.com/marketing/report-brands-falling-fake
-instagram-influencers/

Pearson, B. (2016). Storytizing: What’s next after advertising.
Austin, TX: 1845 Publishing.

Pei, S., Muchnik, L., Andrade, J. S., Jr., Zheng, Z., & Makse, H.
A. (2014). Searching for superspreaders of information in real-
world social media. Scientific Reports, 4, 5547.

Petrescu, M. (2014). Viral marketing and social networks. New
York, NY: Business Expert Press.

Petrescu, M., & Korgaonkar, P. (2011). Viral advertising: Definitional
review and synthesis. Journal of Internet Commerce, 10,
208–226.

Phelps, J. E., Lewis, R., Mobilio, L., Perry, D., & Raman, N.
(2004). Viral marketing or electronic word-of-mouth advertis-
ing: Examining consumer responses and motivations to pass
along email. Journal of Advertising Research, 44, 333–348.

https://firstmonday.org/ojs/index.php/fm/article/view/6650/5598

https://dl.acm.org/citation.cfm?id=1772751

http://www.useit.com/alertbox/participation_inequality.html

https://digiday.com/marketing/report-brands-falling-fake-instagram-influencers/

Himelboim and Golan 13

Phua, J., & Kim, J. J. (2018). Starring in your own Snapchat adver-
tisement: Influence of self-brand congruity, self-referencing
and perceived humor on brand attitude and purchase intention
of advertised brands. Telematics and Informatics, 35, 1524–
1533.

Phua, J., Jin, S. V., & Kim, J. J. (2017). Gratifications of using
Facebook, Twitter, Instagram, or Snapchat to follow brands: The
moderating effect of social comparison, trust, tie strength, and
network homophily on brand identification, brand engagement,
brand commitment, and membership intention. Telematics and
Informatics, 34(1), 412–424.

Porter, L., & Golan, G. J. (2006). From subservient chickens to
brawny men: A comparison of viral advertising to television
advertising. Journal of Interactive Advertising, 6, 30–38.

Publicis. (2017, April 21). Heineken® unveils social experiment for
new Open Your World campaign. Retrieved from https://publicis
.london/news/heineken-unveils-social-experiment-for-new
-open-your-world-campaign/

Rasmussen, L. (2018). Parasocial interaction in the Digital Age: An
examination of relationship building and the effectiveness of
YouTube celebrities. The Journal of Social Media in Society,
7, 280–294.

Scott, D. M. (2015). The new rules of marketing and PR: How to
use social media, online video, mobile applications, blogs,
news releases, and viral marketing to reach buyers directly.
Hoboken, NJ: John Wiley & Sons.

Senft, T. M. (2008). Camgirls: Celebrity and Community in the Age of
Social Networks (Vol. 4). New York: Peter Lang.

Shan, Y., & King, K. W. (2015). The effects of interpersonal tie strength
and subjective norms on consumers’ brand-related eWOM refer-
ral intentions. Journal of Interactive Advertising, 15, 16–27.

Sterne, J. (2010). Social media metrics: How to measure and optimize
your marketing investment. Hoboken, NJ: John Wiley & Sons.

Tsai, W. H. S., & Men, L. R. (2017). Social CEOs: The effects
of CEOs’ communication styles and parasocial interaction
on social networking sites. New Media & Society, 19, 1848–
1867.

Tsiotsou, R. H. (2015). The role of social and parasocial relation-
ships on social networking sites loyalty. Computers in Human
Behavior, 48, 401–414.

Tukachinsky, R. (2010). Para-romantic love and para- friendships:
Development and assessment of a Multiple Parasocial
Relationships Scale. American Journal of Media Psychology,
3, 73–94.

Wallsten, K. (2010). “Yes we can”: How online viewership, blog
discussion, campaign statements, and mainstream media
coverage produced a viral video phenomenon. Journal of
Information Technology & Politics, 7, 163–181.

Wasserman, S., & Faust, K. (1994). Social network analysis:
Methods and applications. New York, NY: Cambridge
University Press.

Watts, D. J., & Dodds, P. S. (2007). Influentials, networks, and
public opinion formation. Journal of Consumer Research, 34,
441–458.

Wells, W., Moriarty, S., & Burnett, J. (2000). Advertising Principles
and Practice. Upper Saddle River, NJ: Prentice Hall.

Yep, J., Brown, M., Fagliarone, G., & Shulman, J. (2017). Influential
players in Twitter networks of libraries at primarily undergraduate
institutions. The Journal of Academic Librarianship, 43, 193–200.

Author Biographies

Itai Himelboim (PhD, University of Minnesota) is an associate pro-
fessor of Advertising and Public Relations and the director of the
SEE Suite, the social media engagement and evaluation lab, at
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 related
to advertising, health, and politics.

Guy J. Golan (PhD, University of Florida) is the director of the
Center for Media & Public Opinion. He has published more than 40
peer-reviewed journal articles focused on media effect, political
communication, and strategic communication.

https://publicis.london/news/heineken-unveils-social-experiment-for-new-open-your-world-campaign/

Annotated Bibliography|FAQs

What is an annotated bibliography?
An annotated bibliography is an analytical summary (or annotation) of the books, scholarly articles, book chapters, or
web documents that you consulted during your research. Writing an annotation encourages you to critically read and
reflect on the points of view and arguments presented in each of your sources and relate them to your research topic.

What do I include in my annotation?

For each source that you consulted:

 RETELL: Briefly summarize the main points*
 what are the author’s arguments (thesis)?
 what research methods were used (survey, questionnaire, interviews,

experiments, self‐assessments)?
 what are the findings or conclusion?

 REFLECT: Comment on the strengths and weaknesses
 RELATE: Assess the relevance of each source

*Note: The length of an annotation can vary (1 paragraph or more). Sometimes you are asked to simply summarize and
other times you may have to compare one source to another. Ask your instructor about their requirements.

If you want to comment on the… Suggested phrases you can use
author’s argument or thesis The researcher argues that….

According to the author, there are two reasons for…
This study suggests that…

strengths and weaknesses The main strength of the article was that….
One limitation of the study was that….

relevance/usefulness The article was useful since it evaluates ….
This book was somewhat relevant to my study because…

conclusions or findings The author concludes that…
The data indicates that . . .
A study exploring…… revealed that…..

How do I format the document?
 List your sources in the appropriate referencing style (APA, MLA, AMA, etc.)

 Alphabetize the list by Author’s last name

 Write an annotation below (or sometimes right after) each reference

 Double‐space the text

 The second and following lines of each reference entry and the annotation are indented so
that only the author’s last names are along the left margin

 In‐text citations are not needed in an annotated bibliography

Sample Annotated Bibliography

Question: What are the benefits of school‐based fitness programs and the challenges of establishing such programs?

Assignment: Submit an annotated bibliography with 10 scholarly sources in APA format. (Final essay due 3 weeks later).

Running head: FITNESS PROGRAMS 1

Title Page

(Ask your instructor for requirements)

School-Based Fitness Programs

Mina Sharma

Sheridan College

References
are listed
alphabetically

Second and
following lines
are indented

FITNESS PROGRAMS 2

Annotated Bibliography

Abudayya, A., Shi, Z., Abed, Y., & Holmboe-Ottesen, G. (2011). Diet,

nutritional status and school performance among adolescents in
Gaza Strip. Eastern Mediterranean Health Journal, 17 (3), 218-
225. Retrieved from
http://www.emro.who.int/publications/emhj/index.asp
Researchers in this study used a “food frequency” questionnaire
to determine the relationship between food intake and school
performance. Results from the 932 surveyed students indicate
that diet and nutrition had a significant impact on school
performance and students’ health. This suggests that……

Racette, S. B., Cade, W. T., & Beckmann, L. R. (2010). School-based

physical activity and fitness promotion. Physical Therapy, 90 (9),
1214-1218. Retrieved from http://ptjournal.apta.org/
The authors conducted a 3 year intervention and assessment of a
school based physical activity program for students in grades K-
12. This report was useful because it gave suggestions on how to
set up a school fitness program. Researchers convinced school
administrators of the program benefits by using evidence from
Cochrane systematic reviews to highlight the health benefits of
physical activity as well as the improved focus that students
would have on their academics. The study found that there was a
6% increase in the number of students who met the physical
fitness standards. One weakness in this study was that it did not
take into account the impact of diet….

Siega-Riz, A.M., El Ghormli, L. Mobley, C. Gillis, B. Stadler, D.

Hartstein, J., …Bridgman, J. (2011). The effects of the
HEALTHY study intervention on middle school student dietary
intakes. The International Journal of Behavioral Nutrition and
Physical Activity 8 (7). doi: 10.1186/1479-5868-8-7

Note:
Writing
should be
double‐
spaced

Describe the
research
methods and
results

Comment on the
usefulness of the
report

Comment on the
results and any
weaknesses of
the study

Adapted from Successful College Writing, 4th edition. Call # PE 14098.M397 2010, Purdue OWL: Annotated Bibliographies retrieved from
https://owl.english.purdue.edu/owl/resource/614/03/ and Deborah Knott’s Writing an Annotated Bibliography retrieved from
http://www.writing.utoronto.ca/advice/specific‐types‐of‐writing/annotated‐bibliography Page 2

http://www.emro.who.int/publications/emhj/index.asp

http://ptjournal.apta.org/

http://www.writing.utoronto.ca/advice/specific

Turn in your highest-quality paper
Get a qualified writer to help you with

“ Introduction and Annotated Bibliography ”

Get high-quality paper

NEW! AI matching with writer

Order a unique copy of this paper

Type of paper needed:

Pages:

600 words

Academic level:

We'll send you the first draft for approval by September 11, 2018 at 10:52 AM

Total price:

$26

Our Services

Introduction and Annotated Bibliography

Order a unique copy of this paper