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.

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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.

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

www.elsevier.com/locate/intmar

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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

    e

    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

    c. Published by Elsevier Inc. All rights reserved.

    Keywords: Mobile commerce; Referral behavior; Sociometric indicators; Mobile viral marketing

    Introduction

    The effectiveness of traditional marketing tools appears to be
    diminishing as consumers often perceive advertising to be
    irrelevant or simply overwhelming in quantity (Porter and
    Golan 2006). Therefore, viral marketing campaigns may provide
    an efficient alternative for transmitting advertising messages to
    consumers, a claim supported by the increasing number of
    successful viral marketing campaigns in recent years. One
    famous example of a viral marketing campaign is Hotmail,
    which acquired more than 12 million customers in less than
    18 months via a small message attached at the end of each
    outgoing mail from a Hotmail account informing consumers
    about the free Hotmail service (Krishnamurthy 2001). In addition
    to Hotmail, several other companies, such as the National
    Broadcasting Company (NBC) and Proctor & Gamble, have
    successfully launched viral marketing campaigns (Godes and
    Mayzlin 2009).

    In general, a viral marketing campaign is initiated by a firm that
    actively sends a stimulus to selected or unselected consumers.
    However, after this initial seeding, the viral marketing campaign

    ⁎ Corresponding author.
    E-mail addresses: 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

    c. Published by Elsevier Inc. All rights reserved.

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

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

    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

    e

    Consumer
    forwards
    message

    n=144

    U
    N

    A
    W

    A

    R

    E
    R

    E
    A

    D
    IN

    G

    S
    TA

    G
    E

    IN

    T

    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).

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      Consumer Decision-making Processes in Mobile Viral Marketing Campaigns
      Introduction
      Related Literature
      Viral Marketing and Factors that Influence Consumer Referral Behavior
      Decision-making Process and Specifics of the Mobile Environment
      Development of Hypotheses
      Psychographic Indicators of Consumer Characteristics
      Sociometric Indicators of Consumer Characteristics
      Empirical Study
      Goal and Research Design
      Measures
      Model
      Results
      Descriptive Statistics and Bias Tests
      Three-stage Decision-making Model
      Entertainment Value and Purposive Value (H1 and H2)
      Usage Intensity (H3)
      Tie Strength (H4)
      Degree Centrality (H5)

      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.

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    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?

    © 2012, American Marketing Association
    ISSN: 0022-2437 (print), 1547-7193 (electronic) 192

    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

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    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.

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    What Makes Online Content Viral? 205

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    https://doi.org/10.1177/2056305119847516

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    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

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    2 Social Media + Society

    media often spreads beyond a single step from the original
    source. Second, in focusing on the characteristics of shared
    content or sharing users, researchers make the assumption
    that all shares are equal in terms of their impact. However,
    sharing-impact varies among users, based on their connectiv-
    ity. Third, the metaphor of virality, the idea that content is
    spread gradually among individuals and their immediate
    contacts, may not fully capture what is often a complex
    multi-actor process of content distribution. Cascades of con-
    tent distribution were found to be centered on a small num-
    ber of distributors, creating a hierarchical, rather than
    egalitarian, pattern of content distribution (Baños, Borge-
    Holthoefer, & Moreno, 2013).

    This study proposes a social networks approach to address
    these limitations, using Heineken’s Worlds Apart campaign
    as a case study. Data are collected for all Twitter users post-
    ing links to the original advertisement on YouTube, and the
    subsequent retweets and mention relationships. While a
    growing body of scholarship examines the potential impact
    of social media influencers in online marketing campaigns,
    they often treat all influencers as one and the same (Evans,
    Phua, Lim, & Jun, 2017; Phua & Kim, 2018).

    We argue that different types of influencers impact social
    networks in different degrees and ways. Informed by a body
    of scholarship in social networks, we propose that there are
    three types of influencers: primary, contextual, and low
    influencers. Primary influencers are hubs, users who attract
    large and disproportionate retweets from other users in the
    network. Contextual influencers play a role of bridges in the
    network by providing context regarding the overall discus-
    sion and thus help to understand the distribution of content
    beyond the quantity of retweets. Low influencers are users
    who shared a link to online content; however, these users
    were neither retweeted nor mentioned by anyone else in the
    network. While low influencers have limited individual con-
    tributions to content distribution, their aggregate influence is
    substantial.

    Social Media Influencers

    An emergent body of scholarship in the field of marketing,
    advertising, and public relations examines the intermediary
    function of influencers between brands and consumers, orga-
    nizations, and stakeholders in social media engagement (De
    Veirman, Cauberghe, & Hudders, 2017; Freberg, Graham,
    McGaughey, & Freberg, 2011; Phua, Jin, & Kim, 2016). At
    the most basic level, influencer is identified by their number
    of followers and their ability to impact social media conver-
    sation regarding brands or topics (Watts & Dodds, 2007).
    While the term social media influencer is ubiquitously
    applied, there are few formal definitions of what an influ-
    encer actually is. Brown and Hayes (2008) defined influenc-
    ers broadly as individuals who hold influence over potential
    buyers of a brand or product to aid in the marketing activities
    of the brand. Others narrow the definition of an influencer to

    reflect on the latest marketing trend in which social media
    celebrities are paid by advertisers to promote products
    (Abidin, 2016; Evans et al., 2017; Senft, 2008).

    Moving beyond definitions, scholars attempt to theorize
    why it is that some social media users grow more influential
    than others via relationship building. To explain the influ-
    ence of influencers, media scholars often depend on the
    parasocial relationship explanation (Daniel, Crawford, &
    Westerman, 2018; Lou & Yuan, 2018; Rasmussen, 2018).
    Moving beyond a temporary parasocial interaction (as origi-
    nally conceptualized by Horton & Wohl, 1956), parasocial
    relationships between audience members and mediated
    characters are formed over a period of time and provide
    audience members with a sense of engagement with on-
    screen characters (Klimmt, Hartmann, & Schramm, 2006;
    Tukachinsky, 2010). In the context of social media, such
    parasocial relationships provide influencers with unique
    social capital that leads to audience trust (Tsai & Men, 2017;
    Tsiotsou, 2015).

    Indeed, the central role of trust in parasocial relation-
    ships may provide a plausible explanation for the influencer
    phenomenon and the rise of influencer marketing (Audrezet,
    De Kerviler, & Moulard, 2018). Trust has been identified as
    a key predictor of several advertising consequences includ-
    ing recall, attitude, and likelihood to share (Cho et al.,
    2014; Lou & Yuan, 2018; Okazaki, Katsukura, & Nishiyama,
    2007). Abidin (2016), building on the concept of parasocial
    relations, identified four ways that influencers appropriated
    and mobilized intimacies: commercial, interactive, recipro-
    cal, and disclosive. Influencers are identified not only based
    on their sheer number of such parasocial relationships, such
    as subscribers or followers on social media, but primarily
    based on their ability to impact social media conversation
    and subsequent behavior regarding brands or topics (Watts
    & Dodds, 2007).

    We propose to complement existing conceptualization of
    influencers by shifting the focus from influencers’ engage-
    ment or the nature of individual connections with them, to
    their ability to reach large, unique, and relevant audiences
    and to shape the conversation about brands and topics. It is
    the distribution of content that allows influencers to influ-
    ence, and therefore provides a key theoretical framework for
    identifying social media influencers. We next discuss viral
    advertising as a theoretical framework for content reach, fol-
    lowed by its limitations. We then take a social networks
    approach to theorize social media influencers, bridging both
    bodies of literature.

    Viral Advertising

    As explained by Golan and Zaidner (2008), there are several
    key differences between viral and traditional advertising.
    First, viral advertising earns audience eyeballs, as opposed to
    paying for them. This is a major departure from the tradi-
    tional advertising exchange, where brands purchase media

    Himelboim and Golan 3

    space and interrupt an audience’s media consumption with
    advertisements. Second, viral advertisements provide such
    increased value to audiences that they transform audiences
    from passive content receivers to active social distributors
    who play a key role in advertisement distribution. Third,
    although there are limited studies speaking to this point, it is
    worth noting that information sharing has been shown to
    increase a user’s followers on Twitter, which is a long-term
    benefit for marketers (Hemsley, 2016).

    What Makes Advertising Go Viral?

    Why do some advertisements receive wide-scale viewership
    via audience distribution, while others do not? Scholars offer
    different approaches to this question, one focusing on con-
    tent characteristics (Brown, Bhadury, & Pope, 2010; Golan
    & Zaidner, 2008; Petrescu, 2014) and another examining
    virality attribute factors such as brand relationships (Hayes
    & King, 2014; Ketelaar et al., 2016; Shan & King, 2015).

    Porter and Golan (2006) specifically identify provocative
    content as contributing to advertising virality. Other studies
    identify appeals to sexuality, as well as shock, violence, and
    other inflammatory content as key elements of message viral-
    ity (Brown et al., 2010; Golan & Zaidner, 2008; Petrescu,
    2014). Eckler and Bolls (2011) argue that the emotional tone
    of advertisement is directly related to audience intention to
    forward ads to others. Yet advertising content, tone, and emo-
    tion cannot fully account for ad virality. Scholars point to a
    variety of other variables significantly related to advertising
    virality including brand relationship (Hayes & King, 2014;
    Ketelaar et al., 2016; Shan & King, 2015), attitude toward the
    ad (Hsieh, Hsieh, & Tang, 2012; Huang, Su, Zhou, & Liu,
    2013), and credibility of the sender/referrer (Cho et al., 2014;
    Phelps, Lewis, Mobilio, Perry, & Raman, 2004).

    Hayes, King, and Ramirez (2016) advanced research on
    viral advertising by illustrating the importance of interper-
    sonal relationship strength in referral acceptance. Their study
    suggested that individuals are motivated to share advertising
    content based on reputational 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

    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/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

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    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.

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

    http://ptjournal.apta.org/

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

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