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Purpose – e purpose of this paper is to analyze the impact of di erent types of content of viral marketing in a popular social networking site. Our research is founded on recent studies which categorize posts on Facebook. Design/methodology/approach – Data for 2583 posts in eight pro les of Brazilian beer brands were coded and analyzed. We used a regression model and Analysis of Variance to establish relationships among independent variables and a dependent variable. Findings – Two hypotheses were supported. ere was a positive relationship between posts of the categories Fan and Promotion and Publicity and viral marketing. Posts of the categories Information and Pool did not have any signi cant e ects, and con rmed previous studies which analyzed likes and comments as dependent variables. Originality/value – Previous studies using the platform did not categorize posts created by brand fans/followers. Our typology is a quantitative improvement in relation to studies with similar objectives. Hence, marketers involved with brand management on Facebook should publish posts which promote the brand and reproduce content generated by people engaged with it if they seek to increase the viralization capacity of such posts.
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545
Review of Business Management., São Paulo, Vol. 18, No. 62, p. 545-569, Oct./Dec. 2016
REVISTA BRASILEIRA DE GESTÃO DE NEGÓCIOS ISSN 1806-4892
REVIEw Of BuSINESS MANAGEMENT e-ISSN 1983-0807
RBGN
Review of Business
Management
DOI: 10.7819/rbgn.v18i62.2620
545
Received on
07/22/2015
Approved on
08/30/2016
Responsible editor:
Prof. Dr. Guilherme de Farias
Shiraishi
Evaluation process:
Double Blind Review
“Engage and attract me, then I’ll share you”: an
analysis of the impact of post category on viral
marketing in a social networking site
Marcos Inácio Severo de Almeida
Milena Costa
Ricardo Limongi França Coelho
Paulo Roberto Scalco
Federal University of Goiás, Faculty of Management,
Accounting and Economics, Goiânia, Goiás, Brazil
Abstract
Purpose – e purpose of this paper is to analyze the impact of dierent
types of content of viral marketing in a popular social networking site.
Our research is founded on recent studies which categorize posts on
Facebook.
Design/methodology/approach – Data for 2583 posts in eight
proles of Brazilian beer brands were coded and analyzed. We used
a regression model and Analysis of Variance to establish relationships
among independent variables and a dependent variable.
Findings – Two hypotheses were supported. ere was a positive
relationship between posts of the categories Fan and Promotion and
Publicity and viral marketing. Posts of the categories Information and
Pool did not have any signicant eects, and conrmed previous studies
which analyzed likes and comments as dependent variables.
Originality/value – Previous studies using the platform did not
categorize posts created by brand fans/followers. Our typology is a
quantitative improvement in relation to studies with similar objectives.
Hence, marketers involved with brand management on Facebook
should publish posts which promote the brand and reproduce content
generated by people engaged with it if they seek to increase the
viralization capacity of such posts.
Keywords Social media marketing; social media metrics; social
networking sites; viral communications; viral marketing.
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Review of Business Management., São Paulo, Vol. 18, No. 62, p. 545-569, Oct./Dec. 2016
Marcos Inácio Severo de Almeida / Milena Costa / Ricardo Limongi França Coelho / Paulo Roberto Scalco
1 Introduction
Ever since it was used to classify the
successful marketing action of a company
providing free email service (Jurvetson & Draper,
1997), the term ‘viral marketing’ has become
popular and has been circulated among managers
anxious to disseminate the most diverse content
to their markets. is triggered the eorts of
researchers to understand the dynamics of the
functioning of viral marketing and people’s
interaction with online content produced by
brands. At present, much of the contact between
brands and their consumers occurs in interactive
online environments in which managers use viral
content as a means of extending the reach of
campaigns (Nelson-Field, Riebe, & Newstead,
2013). is reach usually refers to both economic
response variables such as sales, and non-economic
variables, such as dissemination of information
and creation of awareness (Hinz, Skiera, Barrot,
& Becker, 2011).
Although viral marketing research has been
in existence for almost two decades, there is a lack
of empirical eort to explain the eectiveness of
viral content (Lindgreen, Doeble, & Vanhamme,
2013). Much of this content is published in social
media because viral marketing uses a pre-existing
social network to spread marketing information
via word-of-mouth (WOM) among users of that
social network (Yang, Yao, Ma, & Chen, 2010).
As a result, research conducted with the help of
data from virtual social networks is becoming
increasingly common (Cvijikj & Michahelles,
2013; De Vries, Gensler, & Leeflang, 2012;
Groeger & Buttle, 2014; Kim, Spiller, & Hettche,
2015; Ransbotham, Kane, & Lurie, 2012; Sabate,
Berbegal-Mirabent, Cañabate, & Lebherz, 2014;
Schulze, Schöler, & Skiera, 2014; Smith, Fischer,
& Yongjian, 2012; Swani, Milne, & Brown,
2013), although authors stress that organizations
have not yet been able to measure the eectiveness
of strategies based on these environments
(Homan & Novak, 2012; Kumar, Bhaskaran,
Mirchandani, & Shah, 2013).
Virtual social networks are perfect
platforms for viral marketing as they make it
possible for people to connect on the basis of
broad specialized relationship links, through
virtual communities (Boyd & Ellison, 2007).
Here, individuals spontaneously reproduce
content sponsored by brands which, like a virus,
avail of the virtual social network’s capacity to
multiply (Vilpponen, Winter, & Sundqvist,
2006). Literature about content sharing is
recent and robust enough to explain social and
psychological factors that inuence this behavior
on social media. Oh and Syn (2015) categorized
10 motivational factors responsible for exerting
pressure on information sharing. On Facebook,
the most popular social networking site, they had
identied that social engagement, learning and
altruism are the main drivers of this behavior.
A dierent stream of empirical eorts
concentrates on social media characteristics
that are responsible for facilitating content
sharing. e social aordances of the platform
may enhance user involvement that can lead to
spreading behavior (Oeldorf-Hirsch & Sundar,
2015). However, an important gap remains and
refers to which marketing-content characteristics
can lead to sharing. Our article contributes to
building knowledge about this topic by presenting
a research which set out to identify the eect of
dierent types of content on its dissemination.
It is an investigation based on studies which
categorize marketing-oriented content in virtual
social networks applied to the reality of interaction
between brands and target audiences in these
environments. Research on viral marketing has
produced consistent results, especially with respect
to probabilistic models of reach or the structure
of message dissemination (De Bruyn & Lilien,
2008; Iribarren & Moro, 2011; Van der Lans,
Van Bruggen, Eliashberg, & Wierenga, 2010;
Yang et al., 2010), but has not yet been fully able
to: i) identify the characteristics of the message
spread by users; or ii) explain the power of such
characteristics in sharing the message.
is focus led us to categorize 2583 posts
on the proles of eight brands of Brazilian beer
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“Engage and attract me, then I’ll share you”: an analysis of the impact of post category on viral marketing in a social networking site
on Facebook over a period of three months.
In operational terms, this social network was
chosen because of how representative it is of daily
individual Internet activities, with over a billion
active users per month, 81.7% of whom are
located outside North America (Facebook, 2014).
ey interact directly with companies and brands
which create pages and use this environment
as a tool for contacting their clients in order to
spread advertising campaigns (Boyd & Ellison,
2007; Smith et al., 2012). In theoretical and
empirical terms, this choice was made because,
on Facebook, users can spontaneously share the
content published in company or brand proles in
their own pages (Zarrella & Zarrella, 2010). Our
approach is an important initiative of analyzing
viral marketing, as similar studies based on this
virtual social network only focus on the options
of likes and comments as dependent variables (De
Vries et al., 2012; Swani et al., 2013).
Subsequent sections present viral marketing
as a marketing action, discuss the categorization
of content in virtual social networks and present
the conceptual framework and hypotheses of the
study, based on theoretical assumptions which
consider the effectiveness of online content
according to technical features, functions and
propagandistic appeal. e following sections
discuss the method and results, and are followed
by a discussion of the study’s main limitations
and its theoretical and managerial implications.
2 The State of research on viral
marketing
2.1 e fundamental characteristics of
empirical studies on viral marketing
e term viral marketing describes any
strategy which encourages individuals to spread
a marketing message on these networks, thereby
creating potential for exponential growth in
the exposure and influence of this message
(Camarero & San José, 2011). Research on viral
marketing focuses on two distinct phenomena: i)
the production of marketing content and design
of a reproduction strategy for that content; and
ii) spontaneous dissemination of the message
through electronic WOM, often without control
over the nature or content of that message
(Swanepoel, Lye, & Rugimbana, 2009). As
stated by Camarero and San Jose (2011), there
is no clear denition of the meaning of viral
marketing: there is no consensus on whether
it is a marketing action controlled, sponsored
and triggered by a certain company or a mere
informal process of dissemination and repetition
of content carried out by individuals. Much of
this divergence occurs because viral marketing is a
management-marketing application of the WOM
phenomenon (Modzelewksi, 2000) in which
companies and brands rely on electronic WOM as
a tool to disseminate campaigns (Hennig-urau,
Gwinner, Walsh, & Gremler, 2004).
e origins of viral marketing are directly
related to WOM communication, although
they occur exclusively in virtual environments
(Camarero & San José, 2011). Since the messages
are not requested by the recipient, they can be
ignored. is means that content coming from
close reliable sources is more likely to be accepted
than others from unknown sources. e latter
are classified as less valuable and more risky
information thus being discarded (De Bruyn &
Lilien, 2008). In terms of research conducted
on this subject, investigations alternate between
the investigation by analyzing electronic WOM
communication through individual data, and the
investigation that focuses on the strategy or form
of dissemination of content. Table 1 classies the
theoretical and empirical quantitative research
on viral marketing into four groups, highlights
the main characteristics of each, and presents the
dependent variable usually attributed in these
studies. Exclusively theoretical or qualitative
studies were excluded from this survey for reasons
of suitability to its purpose.
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Marcos Inácio Severo de Almeida / Milena Costa / Ricardo Limongi França Coelho / Paulo Roberto Scalco
Table 1
Main characteristics of studies on viral marketing
Characteristics Dependent variable Main studies
Group 1
Analyses of electronic WOM communication in
blogs and microblogs, such as Twitter, and virtual
social networks, such as Facebook, performed
by analyzing interaction between brands and
individuals. In certain cases, such studies involve
analysis of the sharing of applications or media,
such as video or a game, for example.
Reach; Frequency; Adoption risk. Aral and Walker (2011); Groeger and
Buttle (2014); Jansen, Zhang, Sobel,
& Chowdury (2009); Nelson-Field
et al. (2013); Schulze, Schöler, L., &
Skiera (2014).
Group 2
Correlational and experimental studies investigating
electronic WOM communication by means of
latent variables supported by methods based on
questionnaires, with samples of dierent contexts,
such as college and undergraduate students and
young adults, applied to dierent digital platforms.
Electronic WOM intention; Forwarding
online content; Frequency of sending;
Frequency of visits to digital platforms;
Number of comments written on
opinion platforms; Opinion leadership;
Opinion giving; Opinion passing;
Opinion seeking; Probability of
forwarding.
Camarero and San José (2011);
Chu and Kim (2011); Eckler and
Bolls (2011); Harvey, Stewart, &
Ewing (2011); Hennig-urau et al.
(2004); Ho and Dempsey (2010);
Sohn (2009); Southgate, Westoby,
& Page (2010); Sun, Youn, Wu, &
Kuntaraporn, (2006).
Group 3
Studies which analyze the eects of reviews and
individual incentive strategies for practicing
electronic WOM in environments such as
information-sharing services (Yahoo!Movies) and
online retailer services, such as Amazon.com.
Purchases based on recommendations;
Number of recommendations sent;
Number of online reviews; Placing
of a certain product on the sales
ranking; Reasons for the success of
recommendations; Sales volume.
Ahrens, Coyle, & Strahilevitz (2013);
Chevalier and Mayzlin (2006); Duan,
Gu, & Whinston (2008); Zhang, Ma,
& Cartwright (2013)
Group 4
Research focusing on the analysis and proposal
of analytical models and message propagation
dynamics, by means of theoretical structures based
on social networks and applied to e-mail, instant
messaging and mobile telephony.
Reach; Propagation; Average number of
users reached by a particular message;
Number of recommendations.
Bampo, Ewing, Mather, Stewart, &
Wallace (2008); De Bruyn and Lilien
(2008); Hinz et al. (2011); Iribarren
and Moro (2011); Leskovec, Adamic,
& Huberman (2007); Van der Lans et
al. (2010); Yang et al. (2010).
2.2 Integrating viral marketing research
into social media engagement literature
Table 1 is more comprehensive than
the surveys conducted by either Nelson-Field
et al. (2013) or Vilpponen et al. (2006). is
classication into four groups supports the results
found in the literature review of Chan and Ngai
(2011), which identified a dissemination of
research on the topic. In addition, it exposes the
fragility of dening the terms electronic WOM
communication and viral marketing (Camarero &
San José, 2011), and thereby shows the diculty
of delimiting a widespread phenomenon of
interaction between consumer and company
in digital platforms. Group 1, in particular, is
of special interest to our research as it includes
a smaller number of studies and provides an
opportunity for research in the context of virtual
social networks, such as Facebook.
Nevertheless, it is substantial to fill
the gap presented in Group 1 regarding the
literature about brand engagement in social
media. According to Brodie, Hollebeek, Jurić and
Illić (2011) the conceptual domain of customer
engagement lies on interactive experience and
value co-creation within marketing relationships.
This concept evolved to the investigation of
consumer brand engagement in social media,
dened as a “consumer’s positive valence brand-
related cognitive, emotional and behavioral
activity during or related to focal consumer/brand
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“Engage and attract me, then I’ll share you”: an analysis of the impact of post category on viral marketing in a social networking site
interactions” (Hollebeek, Glynn, & Brodie, 2014,
p. 154). e relationship assumption behind
the concept becomes more intense in virtual
contexts (Dessart, Veloutsou, & Morgan-omas,
2015) where rms and individuals are directly
connected.
Brand-related interactions occur at
dierent levels where the main objective is to
encourage and increase engagement (Azar,
Machado, Vacas-de-Carvalho, & Mendes, 2016).
Brand content acts as a marketing instrument to
provoke response on awareness metrics such
as likes, comments and shares. e primary
objective to brands is to build reach across
these levels (Peters, Chen, Kaplan, Ognibeni, &
Pauwels, 2013). Specically, sharing behavior is
an important phenomenon on social networks
and should be scrutinized by marketers and
researchers to evaluate brand engagement as
it reflects positive attitudes toward a brand
(Homan & Fodor, 2010).
Considering the conceptual denition
of customer engagement proposed by Brodie
et al. (2011) and its evolution to social media
environments introduced by Hollebeek et al.
(2014), we can oer a valuable research insight
about how this phenomenon should be tackled
to unveil viral marketing in social networks. Our
approach resorts on how dierent brand content
is produced and disseminated, in order to trigger
awareness and reach on preexisting networks
built by individuals. Hence, brand engagement is
dened in a behavioral dimension, since it reects
viral marketing activity of brand content in virtual
social networks.
3 Brand content categorization in
virtual social networks
Virtual social networks are groups of
individuals with common interests in which
the basic principle is that the structure of
social relationships is crucial to the content of
these relationships (Wellman, 2001). ey are
changing the ways in which companies interact
with their customers by means of actions which
include recommendations from contacts and
friends, content disseminated and generated by
users and assessments of products and services
(Rohm, Kaltcheva, & Milne, 2013). Such actions
are at the center of individual engagement and
interaction between customer and brand, since
consumers can share recommendations for
purchases or information related to companies or
brands before, during and after the moment of
purchase (Rohm et al., 2013; Schultz & Peltier,
2013). Professionals in the advertising market
perceived the power of virtual social networks
to support the production of advertisements
and targeted advertising (Hoy & Milne, 2010).
However, one challenge for the integration of
these networks is the diculty in quantifying the
return on activities, since companies and brands
have not yet consolidated metrics that could
provide information as to whether the content
posted in these environments has economic or
non-economic eects (Hinz et al., 2011).
Created in 2004 for student purposes,
Facebook is a virtual social network that somehow
moved away from its initial proposal. It is a
digital platform that allows users to create proles
containing personal information, interests,
photographs and invite other users (Smith et al.,
2012). In addition to individuals, companies
and brands can also create proles, which are
used as a tool to approach their clients in order
to disseminate advertising campaigns (Boyd &
Ellison, 2007; Smith et al., 2012). Abram and
Pearlman (2008) present at least two reasons why
a company should keep a Facebook page: rst, it
oers marketers an excellent mechanism for brand
building because of its ability to viralize messages
and content; secondly, it enables companies to
communicate with consumers through interactive
actions. Of the interaction alternatives, the virtual
social network, in particular, provides the option
of sharing so that individuals can spontaneously
reproduce content posted by third parties so
that their friends can view such content. is
consensual reproduction sparks the interest
of researchers studying the dynamics of viral
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Marcos Inácio Severo de Almeida / Milena Costa / Ricardo Limongi França Coelho / Paulo Roberto Scalco
marketing, since the share option is considered
by authors such as Peters et al. (2013) as one
of the outputs of brand management in these
environments.
e research and conceptual frameworks
for characterizing content published on digital
platforms are very diverse. In Facebook specically
posts are one of the items analyzed by researchers.
In these cases, studies focus on features of design
(interactivity with the user, number of media and
text elements, size of post and vividness) and on
content – entertainment, both emotional and
informational (De Vries et al., 2012; Rauschnabel,
Praxmarer, & Ivens, 2012; Swani et al., 2013). e
most striking characteristic of the studies which
analyze sharing as the dependent variable is that
they usually do not attach it to viral marketing.
ey usually refer to it as consumer engagement
(Cvijikj & Michahelles, 2013) or response (Kim
et al., 2015) because the association of sharing
with two other actions in the social network:
comment and liking.
All this research indicates advances in
understanding the types of posting on Facebook,
although it is admitted that more could be known
about the impact they have on sharing, a metric
related to viral marketing. Of the studies which
categorize posts on Facebook, the grand majority
focuses on likes and comments as dependent
variables (De Vries et al., 2012; Sabate et al.,
2014; Swani et al., 2013). e ones which dened
sharing as dependent variable (Kim et al., 2015)
did not concentrate on analyzing it as a form
of viral marketing. Content which is popular
and relevant for users is positively associated
with brand loyalty in virtual social networks
(Erdoğmuş & Çiçek, 2012), since studies indicate
that such content can foster engagement with
customers and produce managerial results, such
as sales (Smith et al., 2012).
4 Conceptual framework and
hypotheses
4.1 Framework overview
Our conceptual framework is based on
the construction of seven hypotheses referring
to post categories which might be used during
interaction between a brand and its target
audience in a virtual social network. Marketers are
increasingly including systems such as Facebook
in their strategies, especially after evidences that
virtual branding actions could raise the levels of
the return on investment (Kumar & Mirchandani,
2012). Our model is based on the argument of
Swanepoel et al. (2009) that viral messages bring
together verbal and visual stimuli. is is why we
consider the classication of post typology and
propose the measurement of this content on a
viral marketing measure (sharing).
Our approach considers brand activity
on Facebook, which may include applications,
present surveys, incorporate images and videos,
and reproduce informative, promotional/
advertising content or ones that are generated
by users. These activities refer to the brand-
related interactions described by the literature
about brand engagement on social media, where
an interactive experience between individuals
and brands (Hollebeek et al., 2014) occurs.
One behavior resulting from this dynamics is
post sharing, when individuals spontaneously
reproduce brand content to their friends and
personal contacts.
ese assumptions led to the creation
of seven categories, a number larger and more
comprehensive than in previous studies. The
underlying logic is that dierent typologies may
produce dierent variability on viral marketing.
Posting types were created after reviewing extant
research which: i) analyzed the eectiveness of
online banners because of their similarity with
brand posts, as both have factors which could
induce people to interact (De Vries et al., 2012;
Fennis & Stroebe, 2010); and ii) classied posts
or viral advertisements according to technical
features, function and propagandistic appeal
(Eckler & Bolls, 2011; Porter & Golan, 2006;
Rauschnabel et al., 2012). Figure 1 summarizes
the conceptual framework, which contains the
hypotheses in which the response variable is viral
marketing, operationally dened as the option
“share” on Facebook.
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“Engage and attract me, then I’ll share you”: an analysis of the impact of post category on viral marketing in a social networking site
Figure 1. Conceptual framework and expected signals of hypothesis testing
4.2 Hypotheses development
This section presents the hypotheses
construction, founded on brand activity on
Facebook. It incorporates social media engagement
in the form of post sharing, as expressed in
Figure 1. is behavioral activity on Facebook
corresponds to a spontaneous dissemination of
the message through electronic WOM, a specic
form of viral marketing (Swanepoel et al., 2009).
e seven hypotheses refer to activities available
for marketers to engage their target audience in
a social media environment: the development of
applications, event announcements, publishing
user-generated content (fans), dissemination
of information, pool announcements, brand
promotion and publicity, and service oering.
Table 2 summarizes hypotheses development.
Applications became part of the Facebook
platform in May 2007. Consequently, third
parties could develop the most diverse software
and add a dimension of use not covered by the
core components of the virtual social network
(Claussen, Kretschmer, & Mayrhofer, 2013).
Russell-Bennett and Neale (2009) state that
applications are an alternative to brand promotion,
since one of the goals of their developers is that
users share them with their contacts. Research
on the use and dissemination of applications
is still in its early stages, although it is already
possible to infer that their use (Eling, Krasnova,
Widjaja, & Bruxmann, 2013) and the sharing of
applications provided by brands are inuenced
by recommendations from friends.
H1: Brand posts categorized as Application
have a linear and positive eect on post
sharing
Facebook oers the option to announce
events so that managers can promote them for
consumers (Lee & Paris, 2013). For marketers, the
fundamental question is to discover how people
perceive such marketing actions (Lee, Xiong, &
Hu, 2012). Events normally appeal to feelings
and emotions (Martensen & Gronholdt, 2008)
and, once a company or brand in virtual social
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Marcos Inácio Severo de Almeida / Milena Costa / Ricardo Limongi França Coelho / Paulo Roberto Scalco
networks mentions them, they can attract the
special attention of the target audience. Lee et al.
(2012) conrmed the hypothesis that user attitude
in an event page exerts a positive impact on the
intention of this user to attend the event. is
result suggests that the relationship between brand
posts of this nature and post sharing is positive.
H2: Brand posts categorized as Event have
a linear and positive eect on post sharing
Accordingly, user-generated content,
created outside of professional routine and
practice, is individually or collectively produced
and can be modified, consumed and shared
(Kaplan & Haenlein, 2010). User-generated
brand content varies between the dierent virtual
social networks (Smith et al., 2012), but one of
its main features is its ability to serve as a credible
source of information for other users (Pavlou
& Dimoka, 2006). On Facebook specically, it
is produced by the user, although the brand or
company can post it. e study by Goh, Heng,
and Lin (2013) helps to understand the economic
behavior resulting from such content, but there
is scope for further research to investigate the
impact of content of this nature on non-economic
variables, such as sharing.
H3: Brand posts categorized as Fan have
a linear and positive eect on post sharing
Dholakia, Bagozzi, and Pearo (2004) argue
that information seeking is an important reason
why people use social networks and participate
in virtual communities. It is not clear if such
participation directly influences interaction
between individuals and brands. Taylor, Lewin,
and Strutton (2011) found a weak, yet positive,
relationship between information content and
individual attitudes to advertisements posted on
social networks. Similarly, from a sample of 402
subjects, Lin and Lu (2011) showed that Facebook
users believe that the use of a social network
improves eciency through sharing information
with others. On the other hand, in the study
by De Vries et al. (2012), the hypothesis that
informative posts inuence likes and comments
was not supported. ese results are diverse from
the ones founded by Cvijikj and Michaehelles
(2013), where informational posts positively
inuence likes and comments, but do not exert
inuence on sharing. Given this divergence of
results, we chose to consider a positive inuence
by posts from this category.
H4: Brand posts categorized as Information
have a linear and positive eect on post
sharing
Another key feature in social media is
interactivity, as feedback mechanisms in real-time
enable users to modify the content structure of
the original message (Homan & Novak, 1996).
Such a feature stimulates consumer engagement
at dierent levels, ranging from participation
through supercial action to commitment to
the co-creation of content (Hennig-urau et
al., 2010). One way of participating is through
surveys in which individuals respond to questions
in virtual social networks. Moderated levels of
interactivity indirectly influence engagement
with advertisements based on the web (Fortin &
Dholakia, 2005), while high levels of interactivity
are negatively related to likes and positively
related to comments (De Vries et al., 2012).
Accordingly, Cvijikj and Michahelles (2013)
found a positive relationship between posts in
the form of sweepstakes (a form of interactivity)
in brand pages and post comments, but no eect
on its sharing. Since the eects of this category
are conicting and partially supported in the
literature about virtual social networks, the same
procedure for the construction of the previous
hypothesis is adopted for this case.
H5: Brand posts categorized as Pool have
a linear and positive eect on post sharing
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“Engage and attract me, then I’ll share you”: an analysis of the impact of post category on viral marketing in a social networking site
Companies spend a considerable part
of their marketing budgets on sales promotion,
which could have direct eects or go beyond an
immediate inuence on sales (Heerde & Neslin,
2008). Promotions are crucial for informing
consumers about the availability of a product
or for creating public awareness of marketing
activities (Bagozzi, 1998). In the context of online
shopping, retailers include sales promotions,
such as discounts and giveaways, to attract
buyers to their websites, as there is evidence
in the literature that promotions are positively
associated with perceived value (Park & Lennon,
2009). In the context of Facebook, De Vries et
al. (2012) found a positive relationship between
a post announcing contests and the number of
likes received for that post.
In the field of Integrated Marketing
Communication (IMC), advertising somewhat
accompanies promotional practices. It is
recognized as an ecient way of communicating
between company and consumer (Stammerjohan,
Wood, Chang, & orson, 2005). Although
advertising guarantees space in the media for
promoting a brand, there is always the inherent
risk of it being something that marketing managers
cannot control (Eisend & Küster, 2011). In the
digital context, the advertising construct receives
new contributions as it is directly related to: i)
content with fun and entertainment; and ii) the
interactivity characteristic of this media. Taylor,
Strutton and ompson (2012) concluded that
entertainment features have positive eects on the
sharing of ads, while Lin and Peña (2011) showed
that in Twitter posts with emotional content
have a greater inuence on the sharing of users.
is type of post has been named Promotion
and Publicity as it includes actions related to
advertising or brand promotion in the virtual
social network. is will be treated in greater
detail in the Method section. It is expected that
such posts will have a positive eect on sharing.
H6: Brand posts categorized as Promotion
and Publicity have a linear and positive
eect on post sharing
Information seeking is one of the four
main reasons why users participate in Facebook
groups (Park, Kee, & Valenzuela, 2009). However,
this information could concern the informative
content of the brand (De Vries et al., 2012), or
could mention an additional service being oered,
such as details about virtual retail shopping
or customer service. The literature which
classies posts in virtual social networks has not
characterized content of this nature, therefore
the impact that this type could have on viral
marketing has not been measured. Identifying
the existence of this impact is important since
there are still divergent results for certain types
of content. For example, although users believe
that by using social networking they can acquire
more information and improve efficiency in
sharing information with others (Lin & Lu,
2011), it was observed that informational post
types do not aect likes or comments (De Vries
et al., 2012). For Hypothesis 7, to specically
address the issue of information about services,
we resorted to arguments of researchers who
claim that additional dimensions of the service
could inuence service satisfaction at individual
level (Raja, Bourne, Gon, Çakkol, & Martinez,
2013).
H7: Brand posts categorized as Service have
a linear and positive eect on post sharing
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Marcos Inácio Severo de Almeida / Milena Costa / Ricardo Limongi França Coelho / Paulo Roberto Scalco
Table 2
Summary of hypotheses development
Hypothesis nº. Hypotheses
1 Brand posts categorized as Application have a linear and positive eect on post sharing
2 Brand posts categorized as Event have a linear and positive eect on post sharing
3 Brand posts categorized as Fan have a linear and positive eect on post sharing
4 Brand posts categorized as Information have a linear and positive eect on post sharing
5 Brand posts categorized as Pool have a linear and positive eect on post sharing
6 Brand posts categorized as Promotion and Publicity have a linear and positive eect on post sharing
7 Brand posts categorized as Service have a linear and positive eect on post sharing
5 Method
is research resorts to secondary data
in order to identify the impact of post typology
on post sharing. is is a common empirical
approach on marketing research using social
network data, as shown by the studies by De
Vries et al. (2012), Rohm et al. (2013), Smith
et al. (2012) and others. Eight ocial proles of
Brazilian beer brands were selected on Facebook.
We decided for this product given the high levels
of consumption in the country, which denotes
an important fast-moving consumer good
(FMCG) to consumer behavior. Brand selection
followed two criteria: (i) the brands chosen
had to have eective participation in the mass
market. For example, they could not be special
beer brands or be exclusively regional; and (ii)
the brands had to participate regularly in social
networking, with a certain frequency of posts.
e main objective in choosing eight dierent
brands was to control the eect of the brand on
the dependent variable, a similar procedure used
by Kim et al. (2015), which controlled product
category. e number of posts (2583), covering
the period between December 2012 – February
2013, is signicantly greater in number than
those analyzed in previous studies on Facebook
(De Vries et al., 2012; Kim et al., 2015; Sabate
et al., 2014; Swani et al., 2013).
5.1 Procedures for collecting data and
denition of variables
Data were collected from the proles of
brands on just one occasion, by means of a web
browser option that saved all the data from each
page. With this procedure the whole content
was saved, up to the point at which the page was
downloaded and stored in an html le. Data for
2583 posts in eight proles of brands were then
systematized into a spreadsheet and coded. One of
the authors was assigned the task of classifying all
posts. is categorization of posts is broader than
previous research on Facebook, and used types
based on the content of the messages published.
roughout the process, the responsible author
received instructions from another researcher,
who at times requested a re-categorization of
certain posts when divergences were observed.
Categorization followed the denitions given in
Table 3 and re-categorization procedure, which
occurred after discussion and agreement between
both researchers, was reproduced from the
literature review conducted by Furrer, omas
and Goussevskaia (2008).
Rauschnabel et al. (2012) limited their
classification of posts according to technical
characteristics such as size, amount of text, media
elements and presence of surveys. A similar
procedure was used by Sabate et al. (2014, p.
1004), who dened Facebook posts by structural
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“Engage and attract me, then I’ll share you”: an analysis of the impact of post category on viral marketing in a social networking site
characteristics, such as containing images, videos
and links, instead “capturing the meaning of the
content itself”. De Vries et al. (2012) used six
types, but included characteristics other than
content, such as position of the brand post on the
page. Smith et al. (2012) formed six categories
and made comparisons between social networks,
such as Facebook, Twitter and YouTube, while
Swani et al. (2013) identied three types: those
which use corporate brand names, those which
refer to emotional content, and those which
make instantaneous references for the purchase
of products or services. Cvijikj and Michahelles
(2013) also classied only three types of content
(entertainment, information and remuneration)
while Kim et al. (2015) dened their typology
using content orientation (task, interaction and
self-oriented).
e classication proposed in our study is a
quantitative improvement on the aforementioned
studies and divides posts into seven categories:
Application, Event, Fan, Information, Pool,
Promotion and Publicity and Service. Table 3
describes the independent variables which are of
a qualitative nature and refer to posts content.
e dependent variable is the number of shares
received by each post. Share is an important
measure of use in virtual social networks and refers
to the extent to which users exchange, distribute
and receive content (Kietzmann, Silvestre, &
McCarthy, 2012). It is, therefore, a non-economic
variable for disseminating information and
creating brand awareness (Hinz et al., 2011).
We decided to present the dependent variable in
its absolute form (in level) in order to identify
the marginal eect of post type on the number
of shares. is choice dierentiates our analyses
from those of De Vries et al. (2012), Kim et al.
(2015) and Sabate et al. (2014), who presented
the dependent variables in logarithmic form. It
is also dierent from the study conducted by
Cvijikj and Michahelles (2013), who presented
the dependent variables in the form of an index.
A group of six control variables was also
considered in order to isolate the actual eect of
each category on the number of shares. Firstly, the
brand was controlled by collecting posts on the
proles of eight beer brands (brand1 to brand8),
as in the study by De Vries et al. (2012) and Kim
et al. (2015), which used product categories as a
control variable. is study was also based on De
Vries et al. (2012) work as it considered the time
of the week when the posting occurred (mid-week
or weekend) as a control variable. Four other
variables were dened: (i) post duration, from
the date on which the content of the proles was
saved by the researchers (duration); (ii) number of
posts of that brand on that same day (quantity);
(iii) time of day (morning, afternoon or evening);
and (iv) the month in which posting occurred
(December, January or February). is total of
six control variables also exceeds previous studies
in the context of Facebook.
Table 3
Independent variables of the study
Variable Description Notation
Application A post with a direct link to an application created by the beer brand holding company,
whose purpose is to provide a software with specic objectives. Creating applications is a
tool oered by Facebook to the brands. For example, an application for monitoring all
carnival dance groups in a large Brazilian city.
app
Event Posts covering the brand or event connected to the brand and which include media, such as
photos and videos. is category mainly contains photos and albums produced during the
2013 Brazilian Carnival and reproduced in proles of the brands on Facebook.
eve
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Marcos Inácio Severo de Almeida / Milena Costa / Ricardo Limongi França Coelho / Paulo Roberto Scalco
Variable Description Notation
Fan A publication with content created by a follower/ fan. is follower is responsible for the
central idea or sends the photograph which is used in the post, and their participation in
the reproduction of this content in the prole of the brand is always mentioned. It also
includes a post in which the brand stimulates the follower to contribute ideas which will be
transformed into a future post, without any previously announced compensation.
fan
Information Publication was classied in this category when the contents presented data about events,
places, opportunities, public gures, musicians, etc., whether connected directly or
otherwise to the brands. For example, informative posts about Carnival street dancing
blocks in a large Brazilian city.
inf
Pool Posts in which questions are directly asked to the followers of the brand with the help of
the platform available on the virtual social network. e follower selects one of the options
oered. e number of options and responses was not considered in this study.
poo
Promotion and
Publicity
Posts which advertise contests and draws, or in other words, which use a reward to stimulate
and encourage the follower to participate. is type also includes posts which promote
the brand in the virtual social network through advertisements which go beyond the
digital sphere (such as those transmitted in traditional media and reproduced in the social
network). In general, they are posts with a fun and similar content aimed at attracting the
attention of their followers.
pp
Service Link or ad which connects directly to the services of the virtual store or to information on
how to acquire a certain product and includes contact phone numbers. Another example
refers to one of the brands which has its own online radio, whose posts make a direct link
to the page on which the radio is broadcasted.
ser
5.2 Empirical model and specication tests
An econometric model was built, where
the dependent variable was the number of
shares, a means of analysis of viral marketing.
The model includes the quantitative and
qualitative independent variables discussed
above. Representations of the intercept and
slope parameters are reproduced in Equation
1. It is worth noting that the equation omits
reference variables, removed to facilitate the
operationalization of the statistical analysis. As
a general principle for the inclusion of dummy
variables which indicate different groups, in
the case of the regression model presenting “g”
groups or categories, the need for the inclusion
of g – 1 variables in the models is emphasized
(Wooldridge, 2013). The reference variables
dened in the model were: “br1” for the beer
brand, “ser” for posts in the services category,
“aft” for the afternoon, “mid” for the mid-week
period and “dec” for the month of December.
e grouping was selected after carrying out
a stepwise procedure where this combination
resulted in a statistically insignicant intercept,
which made it possible to make appropriate
comparisons between the coecients associated
to the groups of dummy variables and the base-
group intercept (Wooldridge, 2013). In short, the
statistical insignicance of the constant enabled
comparisons between the coecients of the types
of post when they were signicant.
sharing = β0+ β1app + β2eve + β3fan + β4inf
+ β5poo + β6pp + β7br2 + β8br3 + β9br4 +
β10br5 + β11br6 + β12br7 + β13br8+ β14dur
+ β15qua + β16mor + β17evn+ β18wee + β19jan
+ β20fev + u (1)
Assumption tests were carried out before
choosing the analytical model. First, tests were
done to identify the presence of heteroskedasticity.
In both cases, the null hypothesis was rejected for
the presence of constant variance of residuals. e
White (1980) test returned a Chi value of 57.53 (p
<0.01), while the Breusch and Pagan (1979) test
returned a Chi value of 3803.95 (p <0.01). e
normality of the residuals test (Chi = 23758.9, p
<0.01) corroborates these results which show that
the residuals do not have normal distribution.
ese procedures underlay the choice of a robust
estimation model, using Generalized Least
Squares (GLS).
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“Engage and attract me, then I’ll share you”: an analysis of the impact of post category on viral marketing in a social networking site
6 Results
Table 4 summarizes the descriptive
statistics. We chose not to present the statistics
for the control variables related to the brands
(br1 through br8) because they were omitted in
the sample. A post receives, on average, 1924.54
shares, with a standard deviation of 4712.65.
Of the qualitative independent variables used,
the greatest number of posts was categorized in
the Publicity and Promotion type (1801), about
69% of all the contents analyzed in the study.
is implies that, in general, brands use Facebook
to promote content on entertainment and to
advertise draws and contests. Next comes the posts
produced by fans (294), followed by those which
communicate events related to the brands (191).
e least used contents are those which advertise
surveys (22) and applications (23), respectively.
In the case for post typology, Table 4 presents
descriptive statistics of shares weighted by the
number of posts inside each category.
e results of both quantitative control
variables show that the average exposure time
of publications was 61.57 days. is number is
a direct function of the period over which the
investigation extended, namely, three months, and
showed a deviation of 25.44. e quantity (qua)
variable indicates that, on average, the brands
make 4.72 posts per day, with a standard deviation
of 2.83. e qualitative control variables indicate
that the majority of posts (1115, 43%) occur in
the afternoon, between 12:00 am and 5:59 pm,
and midweek (1509, 58%). ese results are
important because they represent an indication of
the activity of brands in the virtual social network.
Further details on the relationship among control
variables are expressed on the correlation matrix.
Table 4
Descriptive statistics
Variable Mean Std. err. Freq
Dependent variable
sharing 1924.54 4712.65 2583
Independent variablesa
app
eve
fan
inf
poo
pp
ser
331.91
363.56
3352.91
419.44
0.45
2084.98
985.03
332.58
832.09
4978.63
987.68
0.21
5160.45
1316.34
23
191
294
168
22
1801
84
Control variables
dur
qua
mor
aft
evn
mid
wee
dec
jan
feb
61.57
4.72
0.32
0.43
0.24
0.58
0.41
0.34
0.32
0.33
25.44
2.83
0.46
0.49
0.42
0.49
0.49
0.47
0.46
0.47
2583
2583
848
1115
620
1509
1074
879
850
854
Note.
a
Table 4 presents descriptive statistics of shares weighted by the number of posts inside each category. Grand mean
in this case is unconditional sharing mean (1924.54)
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Marcos Inácio Severo de Almeida / Milena Costa / Ricardo Limongi França Coelho / Paulo Roberto Scalco
Table 5 presents inferential statistics and
shows that the model adjusts adequately to the
data. e variables included explain 16% of the
variability in the shares of posts. Although low,
which makes predictive structures less accurate,
it explains more than the likes model of De Vries
et al. (2012), for example. It is important to
remember, however, that these two studies have
slightly dierent goals. e value of R-squared
and the use of variables is sustained by the overall
signicance of the regression which returned
a F-statistic of 31.24 (p <0.01). e constant
is not signicant because the authors decided
on a grouping of reference variables which
would give an intercept which would not be
statistically signicant. is procedure ensures
that the marginal eect of the post types can be
identied. It is important to mention that the
tests for variance ination factors (VIF) identied
a weak presence of multicollinearity between the
independent variables. Only the Promotion and
Publicity (VIF = 7.21) variable presented a result
close to 8. Numbers greater than 10 could mean
a problem of collinearity. However, the values
varied between 1 and 5 with data from beer posts,
which denotes an acceptable collinearity among
the regressors (Gujarati & Porter, 2008).
e results of the estimates for the control
variables show that an eect of a major brand
really exists. Of the eight brands chosen to control
this eect, statistical signicance was observed in
six, which had positive (br2, br3, br4 and br8) or
negative (br5 and br7) coecients. ese results
show that the activity of a brand on Facebook may
reect the market share of this brand. However,
it would be necessary to confront and compare
these data, which was not possible in this study.
In relation to the control variables, it was also seen
that, on average, the morning period posts receive
more shares than evening or night periods and
similarly posts published in December received
more shares than those of January or February.
All these coecients were statistically signicant
at a 99% level, with the exception of the duration
of the post (dur): the exposure time reduced the
number of shares at a 90% condence level.
6.1 Results of hypothesis tests
As already mentioned, the analysis of
the Constant (Const.) shows an intercept which
is not statistically signicant. at means that
there is no relationship between the Service (ser)
type and the number of shares. is category is
omitted from Table 5 because it is the reference
category of the type variables which is reproduced
in the intercept. In statistical terms, its eect on
viral marketing is zero and with that result one
can say that there was no support for Hypothesis
7. Although there is consistent literature on the
impact of additional elements and dimensions
of services at individual and organizational levels
(Raja et al., 2013), this eect is not reproduced
in the virtual social networks. Posts that refer to
Services, such as virtual store or customer service,
do not inuence the share of this post. It can even
be armed that the values of br1, aft, mid and
dec variables, all reproduced in the intercept, are
also statistically equal to zero and do not have any
linear impacts on the dependent variable.
Hypothesis 1 was not supported either,
since the Application (app) type posts do not
have a positive eect on the dependent variable.
e use of applications increases user engagement
and improves the reviews of users (Claussen et
al., 2013) when the software developer is trusted
(Eling et al., 2013). Although applications are
crucial mechanisms for the dissemination of
content (Eling et al., 2013), their use in brand
posts does not have a signicant impact on post
sharing. Hypothesis 2 was not supported either
because the coecient of the variable Event (eve)
is statistically equal to zero. e event pages
on Facebook could even encourage individuals
to participate in such events outside the social
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“Engage and attract me, then I’ll share you”: an analysis of the impact of post category on viral marketing in a social networking site
network (Lee & Paris, 2013), although they do
not directly lead to post sharing which mentions
a certain event, when this content is published
on brand proles.
There was a positive and statistically
signicant eect at a 99% condence level of
the category Fan (fan) on sharing. is result
guarantees conrmation of Hypothesis 3 and is
the rst evidence of viral marketing on Facebook.
Many studies focus on the impact of user-
generated content on economic consequences,
such as sales. e literature shows a positive eect
on the aggregate sales of products such as lms,
books and video games (Goh et al., 2013). Our
study provides a consistent analytical basis for the
fact that posts classied in this category also have
a positive impact on non-economic consequences,
such as shares, a phenomenon which occurs in
virtual social networks such as Facebook. is
impact is important because the main objectives
of a viral marketing campaign are to disseminate
information, create awareness and cultivate brand
perceptions (Hinz et al., 2011).
Consumers seek information about brands
and in certain cases this search can be both time
and eort-consuming (Kiel & Layton, 1981).
Unlike what is observed at individual level and
outside the virtual context, providing information
about brands in virtual social networks does not
guarantee a response in non-economic measures.
In our study, brand posts classied as Information
(inf) did not have any positive eects on shares.
ese results do not support Hypothesis 4 and
conrm the results of the study by De Vries et
al. (2012) and Cvijikj and Michaehelles (2013)
in which the authors, respectively, found no
relationship between informative content, likes,
comments and shares and concluded that this
is not the characteristic which determines the
popularity of a post.
Traditionally, marketing managers used
the one-to-many communication format to
disseminate brand values to the target public, but
the advent of virtual social networks obliged them
to incorporate this media into the communication
mix (Gensler, Völckner, Liu-Thompkins, &
Wiertz, 2013). In one-to-many communication
formats, there is a relationship between an
individual and a mediator environment which
provides high levels of interactivity (Homan &
Novak, 1996) as for example, through surveys. De
Vries et al. (2012) found a negative relationship
between high levels of interactivity and likes,
and a positive relationship between high levels
and comments. When specically addressing the
context of viral marketing, there were no positive
eects of the Pool (poo) type of post on sharing,
which do not support Hypothesis 5 in our study.
e second indication of viral marketing
on Facebook stems from the positive eect of
the Promotion and Publicity (pp) category. is
effect supports Hypothesis 6 and shows that
for posts which advertise contests and draws,
and promote a brand with entertaining content
there is an average increase of 2419.25 shares.
is relationship is statistically signicant at a
99% level and corroborates the studies of Taylor
et al. (2012) on Facebook, and Lin and Peña
(2011) on Twitter. The former showed that
entertainment content in advertisements in the
virtual social network exerts a positive inuence
on the attitude of service users, while the latter
showed that brands use positive tones in the
management of socio-emotional communication
with their audiences. Interestingly, the results
of this type were dierent from those found in
the De Vries et al. (2012) research, which did
not nd any signicant relationship between
posts with content referring to entertainment
and likes and comments. Conversely, Cvijikj
and Michaehelles (2013) found a signicant and
positive relationship between entertainment posts
and likes, comments and shares. ese results
indicate the need for further research about post
typology on virtual social networks.
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Marcos Inácio Severo de Almeida / Milena Costa / Ricardo Limongi França Coelho / Paulo Roberto Scalco
Table 5
Results for the independent and control variables coecients
Variable B SE p-value
Const.
Independent variables
app
eve
fan
inf
poo
pp
-322.71
-179.90
-162.24
2226.59
115.64
667.78
2418.25
49.25
327.36
326.49
382.23
281.44
448.13
295.99
0.51
0.58
0.61
0.00
0.68
0.13
0.00
Control variables
br2
br3
br4
br5
br6
br7
br8
dur
qua
mor
evn
wee
jan
feb
2086.40
1616
1160.84
-790.64
-280.95
-648.86
4718.05
-10.87
-64.13
589.61
38.64
151.78
-730.67
-986.72
314.52
265.85
276.70
225.16
244.22
227.89
555.87
6.12
46.67
204.58
228.75
187.64
242.77
293.65
0.00
0.00
0.00
0.00
0.25
0.00
0.00
0.07
0.16
0.00
0.86
0.41
0.00
0.00
Goodness of t
Adjust R-square: 0.16
F = 31.24, p < 0.01
Note. Figures in bold: p-value <0.00; in italic: p-value <0.10.
6.2 Additional analysis
We concentrated on Fan and Promotion
and Publicity typologies to conduct additional
analysis on our results. The objective was to
explore the results found in our inferential
model. At rst, we performed simple independent
samples T tests to compare means of these two
groups, considering the whole set of control
variables. Considering the postings of the eight
brands on the dataset and post quantity per day,
means where statistically dierent at a 99% level.
For time of the day, means where dierent at a
99% (for morning), 95% (afternoon) and 90%
(evening) levels. e results suggest no statistical
dierence in means for post duration, time of the
week and month. Table 6 summarizes the results
of the T tests.
A second procedure involved an ANOVA
in order to meet two objectives: rst, to generate
estimated marginal means and, second, to
identify the partial eect for each post typology.
In this model, we used sharing as dependent
variable and post typologies as factor variables.
We have tried to run an ANCOVA using post
quantity and duration as covariates, but basic
assumptions of this model (independence of the
covariate and treatment eect and homogeneity
of regression slopes) were violated (Field, 2009).
Figure 2 reveals the rst output from the ANOVA,
indicating that Fan and Promotion and Publicity
posts are indeed way above from the baseline, both
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“Engage and attract me, then I’ll share you”: an analysis of the impact of post category on viral marketing in a social networking site
generating more than at least 2000 shares, while
Application, Event, Information and Pool are near
Table 6
Results for T tests using two groups: fan and promotion and publicity typologies
Variable Equal variances
assumed?at value p-value Mean dierence
dur yes -.49 .62 -.80
qua no 8.47 .00 1.11
mor no -6.03 .00 -.15
aft no 3.49 .00 .10
eve no 1.76 .07 .04
mid yes .73 .46 .02
wee yes -.73 .46 -.02
dec yes -.82 .40 -.02
jan yes .86 .38 .02
feb no -.01 .98 -.00
Note. Figures in bold: p-value <0.001; in bold and italic: p-value<0.05; in italic: p-value <0.10
a
Provided by Levene’s hypothesis test for equality of variances (We used a 95% condence level for this test)
the point marked at zero. Postings categorized as
Service are only slightly above this baseline.
Figure 2. Estimated marginal means considering dierent post typologies
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Marcos Inácio Severo de Almeida / Milena Costa / Ricardo Limongi França Coelho / Paulo Roberto Scalco
Table 7 complements Figure 2 and presents
the results from the ANOVA model. It reveals that
only coecients from Fan and Promotion and
Publicity typologies are statistically dierent from
Service, the reference category. Fan is signicant
at a 99% level, while Promotion and Publicity, at
a 95% level. Partial eects from the rst typology
is three times higher than the latter, clearly
indicating a patch of how marketers should invest
on content categorization on Facebook, in order
to enhance the likelihood of viral marketing.
Table 7
Parameter estimates for the ANOVA model
Parameter B t value p-value Partial Eta Squared
Intercept 985.03 1.94 .05 .001
Application -653.12 -.59 .55 .000
Event -621.47 -1.02 .30 .000
Fan 2367.87 4.11 .00 .007
Information -565.58 621.03 .36 .000
Pool -984.99 -.885 .37 .000
Promotion and Publicity 1099.94 2.12 .03 .002
Service 0a
Note. Figures in bold: p-value <0.001; in bold and italic: p-value<0.05
a
is parameter is set to zero because it is redundant (reference category)
7 Discussion and managerial
implications
Virtual social networks like Facebook
have revolutionized the ways how organizations
relate to their markets and to society in general,
and have created a world of new possibilities and
challenges for various aspects of the company
(Aral, Dellarocas, & Godes, 2013). As these media
gain popularity among users, managers seek ways
to include these networks in marketing strategy
in order to engage and inuence their target
audiences (Homan & Novak, 2012). ese
networks lead to the active participation of the
individual and guarantee high levels of network
interconnectivity (Hennig-urau, Hofacker, &
Bloching, 2013) which transform key aspects
of marketing and consumer behavior. However,
despite such progress, many questions remain
unanswered (Aral et al., 2013) and researchers
still study more eective metrics for managing
brands in these environments (Peters et al., 2013).
One of the best known response
mechanisms on Facebook is the option of sharing.
People who access virtual social networks can
produce and modify content, but above all, they
have the option of sharing it (Peters et al., 2013).
Kietzmann et al. (2012) mention one major
implication of sharing in virtual social networks:
the need to discover what forces induce people to
disseminate such content. is need is supported
by theoretical gaps seen in the literature on viral
marketing. Bampo, Ewing, Mather, Stewart and
Wallace (2008), for example, show the need for
intensied analysis of managerial interference
in viral marketing on Facebook while Schulze
et al. (2014) highlights dierences on sharing
mechanisms for utilitarian and low-utilitarian
products, such as games and music services.
Our study contributes to the current
research on viral marketing by analyzing the
impact of brand content in virtual social networks.
First, our typology, based on the building of seven
categories of post, is a quantitative improvement
in relation to studies with similar objectives, as
it includes a more comprehensive and extensive
number of contents in interactions between brand
and target audiences. Secondly, the results of the
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“Engage and attract me, then I’ll share you”: an analysis of the impact of post category on viral marketing in a social networking site
hypotheses tests, summarized in Table 8 show the
importance of user-generated content and that
which mention oers or promotes the brand on the
social network. Previous studies using Facebook
platform did not categorize posts created by brand
fans/followers. e main managerial implications
refer to these two categories, which showed linear
and positive impacts on the number of shares:
marketers engaged in brand management on
Facebook should publish posts which promote the
brand and reproduce content generated by people
engaged with the brand if they are to increase the
viralization capacity of such posts.
Table 8
Results of hypothesis testing
Hypothesis Post typology Expected signal Impact Interpretation
1
2
3
4
5
6
7
Application
Event
Fan
Information
Pool
Promotion and Publicity
Service
Positive
Positive
Positive
Positive
Positive
Positive
Positive
None
None
Positive
None
None
Positive
None
Not supported
Not supported
Supported
Not supported
Not supported
Supported
Not supported
8 Limitations and future research
The main limitation of this study is
related to the reduced number of samples in some
categories. Pool (22) and Application (23) come
close to one percent of posts analyzed. As they are
less frequent, a study which would increase their
number would require more time during the data
collection phase. Another major consideration
which limits the scope of our study is the inclusion
of many control variables of a qualitative nature.
Further studies should incorporate, for example,
the length of the message in characters and try
to consolidate all the variables used in earlier
studies. Finally, the Least Squares method limits
the analysis to a linear function. New methods
could improve the explanatory power of viral
marketing on Facebook.
Future studies should extend and improve
the presented model. Firstly, it would be
appropriate to test the share variable in logarithmic
form in order to analyze the rate of variation
of viral marketing, as previous studies did.
Nevertheless, to make that feasible, it is suggested
that a time restriction should be included in
order to analyze the evolution of this rate over
time. Secondly, new models could include other
non-economic response variables, such as likes
and comments, and consider the endogeneity
of variables such as likes, comments and shares.
e empirical structure would be grounded on
systems of equations which, theoretically, seem
to better capture the dynamics of interaction
between individuals and brands on Facebook, as
those who enjoy a particular post seem to be more
inclined to comment on and share it.
References
Abram, C., & Pearlman, L. (2008). Facebook for
dummies. Hoboken, NJ: John Wiley & Sons.
Ahrens, J., Coyle, J. R., & Strahilevitz, M. A.
(2013). Electronic word of mouth: e eects of
incentives on e-referrals by senders and receivers.
European Journal of Marketing, 47(7), 1034-1051.
Aral, S., Dellarocas, D., & Godes, D. (2013).
Social media and business transformation: A
framework for research. Information Systems
Research, 24(1), 3-13.
Aral, S., & Walker, D. (2011). Creating social
contagion through viral product design: A
randomized trial of peer inuence in networks.
Management Science, 57(9), 1623-1639.
564
Review of Business Management., São Paulo, Vol. 18, No. 62, p. 545-569, Oct./Dec. 2016
Marcos Inácio Severo de Almeida / Milena Costa / Ricardo Limongi França Coelho / Paulo Roberto Scalco
Azar, S. L., Machado, J. C., Vacas-de-Carvalho,
L., & Mendes, A. (2016). Motivations to interact
with brands on Facebook: Towards a typology of
consumer-brand interactions. Journal of Brand
Management, 23(2), 157-178.
Bagozzi, R. P. (1998). Marketing management.
Upper Saddle River, NJ: Prentice-Hall.
Bampo, M., Ewing, M. T., Mather, D. R.,
Stewart, D., & Wallace, M. (2008). e eects
of the social structure of digital networks on
viral marketing performance. Information Systems
Research, 19(3), 237-290.
Boyd, D. M., & Ellison, N. B. (2007). Social
network sites: Denition, history, and scholarship.
Journal of Computer-Mediated Communication,
13(1), 210-230.
Breusch, T. S., & Pagan, A. R. (1979). A simple
test for heteroscedasticity and random coecient
variation. Econometrica, 47(5), 1287-1294.
Brodie, R. J., Hollebeek, L. D., Jurić, B.,
& Ilić, A. (2011). Customer engagement:
Conceptual domain, fundamental propositions,
and implications for research. Journal of Service
Research, 14(3), 252-271.
Camarero, C., & San José, R. (2011). Social
and attitudinal determinants of viral marketing
dynamics. Computers in Human Behavior, 27(6),
2292-2300.
Chan, Y. Y. Y., & Ngai, C. E. W. T. (2011).
Conceptualising electronic word of mouth
activity. Marketing Intelligence & Planning, 29(5),
488-516.
Chevalier, J. A., & Mayzlin, D. (2006). e eect
of word of mouth on sales: Online book reviews.
Journal of Marketing Reserach, 43(3), 345-354.
Chu, S., & Kim, Y. (2011). Determinants of
consumer engagement in electronic word-of-
mouth (eWOM) in social networking sites.
International Journal of Advertising, 30(1), 47-75.
Claussen, J., Kretschmer, T., & Mayrhofer, P.
(2013). e eects of rewarding user engagement:
e case of Facebook apps. Information Systems
Research, 24(1), 186-200.
Cvijikj, I. P., & Michahelles, F. (2013). Online
engagement factors on Facebook brand pages.
Social Network Analysis and Mining, 3(4), 843-861.
De Bruyn, A., & Lilien, G. L. (2008). A multi-
stage model of word-of-mouth inuence through
viral marketing. International Journal of Research
in Marketing, 25(3), 151-163.
De Vries, L., Gensler, S., & Leeang, P. S. H.
(2012). Popularity of brand posts on brand fan
pages: An investigation of the eects of social
media marketing. Journal of Interactive Marketing,
26(2), 83-91.
Dessart, L., Veloutsou, C., & Morgan-omas, A.
(2015). Consumer engagement in online brand
communities: A social media perspective. Journal
of Product & Brand Management, 24(1), 28-42.
Dholakia, U. M., Bagozzi, R. P., & Pearo, L. K.
(2004). A social inuence model of consumer
participation in network and small-group-based
virtual communities. International Journal of
Research in Marketing, 21(3), 241-263.
Duan, W., Gu, B., & Whinston, A. B. (2008). e
dynamics of online word-of-mouth and product
sales: An empirical investigation of the movie
industry. Journal of Retailing, 84(2), 233-242.
Eckler, P., & Bolls, P. (2011). Spreading the virus
emotional tone of viral advertising and its eect
on forwarding intentions and attitudes. Journal
of Interactive Advertising, 11(2), 1-11.
Eisend, M., & Küster, F. (2011). e eectiveness
of publicity versus advertising: A meta-analytic
565
Review of Business Management., São Paulo, Vol. 18, No. 62, p. 545-569, Oct./Dec. 2016
“Engage and attract me, then I’ll share you”: an analysis of the impact of post category on viral marketing in a social networking site
investigation of its moderators. Journal of the
Academy of Marketing Science, 39(6), 906-921.
Eling, N., Krasnova, H., Widjaja, T., & Buxmann,
P. (2013). Will you accept an app? Empirical
investigation of the decisional calculus behind the
adoption of applications on Facebook. Proceedings
of the International Conference of Information
Systems, Milan, Italy.
Erdogmus, I. E., & Çiçek, M. (2012). e impact
of social media marketing on brand loyalty.
Procedia – Social and Behavioral Sciences, 58,
1353-1360.
Facebook. (2014). Facebook newsroom: Company
info. Retrieved from http://newsroom.fb.com/
company-info/
Fennis, B. M., & Stroebe, W. (2010). e psychology
of advertising. New York, NY: Psychology Press.
Field, A. (2009). Discovering statistics using SPSS.
ousand Oaks, CA: Sage Publications.
Fortin, D. R., & Dholakia, R. R. (2005).
Interactivity and vividness effects on social
presence and involvement with a web-based
advertisement. Journal of Business Research, 58(3),
387-396.
Furrer, O., omas, H., & Goussevskaia, A.
(2008). The structure and evolution of the
strategic management eld: A content analysis
of 26 years of strategic management research.
International Journal of Management Reviews,
10(3), 1-23.
Gensler, S., Völckner, F., Liu-ompkins, T.,
& Wiertz, C. (2013). Managing brands in the
social media environment. Journal of Interactive
Marketing, 27(4), 242-256.
Goh, K., Heng, C., & Lin, Z. (2013). Social
media brand community and consumer behavior:
Quantifying the relative impact of user- and
marketer-generated content. Information Systems
Research, 24(1), 88-107.
Groeger, L., & Buttle, F. (2014). Word-of-mouth
marketing: Towards an improved understanding
of multi-generational campaign reach. European
Journal of Marketing, 48(7/8), 11860-1208.
Gujarati, D., & Porter, D. (2008). Basic
econometrics. New York, NY: McGraw-Hill.
Harvey, C. G., Stewart, D. B., & Ewing, M. T.
(2011). Forward or delete: What drives peer-to-
peer message propagation acrosss social networks?
Journal of Consumer Behavior, 10(6), 365-372.
Heerde, H. J., & Neslin, S. A. (2008). Sales
promotion models. In B. Wierenga (Ed.).
Handbook of marketing decision models (107-162).
New York, NY: Springer.
Hennig-urau, T., Malthouse, E. C., Friege, C.,
Gensler, S., Lobschat, L., Rangaswamy, A., &
Skiera, B. (2010). e impact of new media on
customer relationships. Journal of Service Research,
13(3), 311-330.
Hennig-urau, T., Gwinner, K. P., Walsh, G.,
& Gremler, D. D. (2004). Electronic word-of-
mouth via consumer-opinion platforms: What
motivates consumers to articulate themselves on
the Internet? Journal of Interactive Marketing,
18(1), 38-52.
Hennig-Thurau, T., Hofacker, C. F., &
Bloching, B. (2013). Marketing the pinball way:
Understanding how social media change the
generation of value for consumer and companies.
Journal of Interactive Marketing, 27(4), 237-241.
Hinz, O., Skiera, B., Barrot, C., & Becker, J. U.
(2011). Seeding strategies for viral marketing:
An empirical comparison. Journal of Marketing,
75(6), 55-71.
Ho, J. Y. C., & Dempsey, M. (2010). Viral
marketing: Motivations to forward online
566
Review of Business Management., São Paulo, Vol. 18, No. 62, p. 545-569, Oct./Dec. 2016
Marcos Inácio Severo de Almeida / Milena Costa / Ricardo Limongi França Coelho / Paulo Roberto Scalco
content. Journal of Business Research, 63(9), 1000-
1006.
Homan, D. L., & Fodor, M. (2010). Can you
measure the ROI of your social media marketing?
MIT Sloan Management Review, 52(1), 41-49.
Hoffman, D. L., & Novak, T. P. (1996).
Marketing in hypermidia computer-mediated
environments: Conceptual foundations. Journal
of Marketing, 60(3), 50-68.
Homan, D. L., & Novak, T. P. (2012). Toward
a deeper understanding of social media. Journal
of Interactive Marketing, 26(2), 67-70.
Hollebeek, L. D., Glynn, M. S., & Brodie, R. J.
(2014). Consumer brand engagement in social
media: Conceptualization, scale development
and validation. Journal of Interactive Marketing,
28(2), 149-165.
Hoy, M. G., & Milne, G. (2010). Gender
dierences in privacy-related measures for young
adult Facebook users. Journal of Interactive
Advertising, 10(2), 28-45.
Iribarren, J. L., & Moro, E. (2011). Branching
dynamics of viral information spreading. Physical
Review E, 84(4), 046116.
Jansen, B., Zhang, M., Sobel, K., & Chowdury,
A. (2009). Twitter power: Tweets as electronic
word of mouth. Journal of the American Society
for Information Science and Technology, 60(11),
2169-2188.
Jurvetson, S. & Draper, T. (1997). Viral marketing:
Viral marketing phenomenon explained. Retrieved
from http://www.d.com/news/article_26.shtml/
Kaplan, A. M., & Haenlein, M. (2010). Users of
the world, unite! e challenges and opportunities
of social media. Business Horizons, 53(1), 59-68.
Kiel, G. C., & Layton, A. (1981). Dimensions of
consumer information seeking behavior. Journal
of Marketing Research, 18(2), 233-239.
Kietzmann, J. H., Silvestre, B. S., & McCarthy,
I. P. (2012). Unpacking the social media
phenomenon: Towards a research agenda. Journal
of Public Aairs, 12(9), 109-119.
Kim, D., Spiller, L., & Hettche, M. (2015).
Analyzing media types and content orientations
in Facebook for global brands. Journal of Research
in Interactive Marketing, 9(1), 4-30.
Kumar, V., Bhaskaran, V., Mirchandani, R., &
Shah, M. (2013). Creating a mensurable social
media marketing strategy: Increasing the value
and ROI of intangibles and tangibles for Hokey
Pokey. Marketing Science, 32(2), 194-212.
Kumar, V., & Mirchandani, R. (2012). Increasing
the ROI of social media marketing. MIT Sloan
Management Review, 54(1), 55-61.
Lee, W., & Paris, C. M. (2013). Knowledge
sharing and social technology acceptance model:
Promoting local events and festivals through
Facebook. Tourism Analysis, 18(4), 457-469.
Lee, W., Xiong, L., & Hu, C. (2012). e eect of
Facebook users’ arousal and valence on intention
to go to the festival: Applying and extension of the
technology acceptance model. International Journal
of Hospitality Management, 31(3), 819-827.
Leskovec, J., Adamic, L. A., & Huberman, B. A.
(2007). e dynamics of viral marketing. ACM
Transactions on the Web, 1(1), 1-46.
Lin, K.-Y., & Lu, H.-P. (2011). Why people
use social networking sites: An empirical study
integrating network externalities and motivation
theory. Computers in Human Behavior, 27(3),
1152-1161.
Lin, J. S., & Peña, J. (2011). Are you following
me? A content analysis of TV networks brand
communication on Twitter. Journal of Interactive
Advertising, 12(1), 17-29.
567
Review of Business Management., São Paulo, Vol. 18, No. 62, p. 545-569, Oct./Dec. 2016
“Engage and attract me, then I’ll share you”: an analysis of the impact of post category on viral marketing in a social networking site
Lindgreen, A., Doeble, A., & Vanhamme, J.
(2013). Word-of-mouth and viral marketing
referrals: What do we know? European Journal of
Marketing, 47(7), 1028-1033.
Martensen, A., & Gronholdt, L. (2008). How events
work: Understanding consumer responses to event
marketing. Innovative Marketing, 4(4), 44-56.
Modzelewski, F. M. (2000). Finding a cure for
viral marketing. Direct Marketing News, 11(9).
Nelson-Field, K., Riebe, E., & Newstead, K.
(2013). The emotions that drive viral video.
Australasian Marketing Journal, 21(4), 205-211.
Oeldorf-Hirsch, A., & Sundar, S. S. (2015).
Posting, commenting, and tagging: Eects of
sharing news stories on Facebook. Computers in
Human Behavior, 44, 240-249.
Oh, S., & Syn, S. Y. (2015). Motivations for
sharing information and social support in social
media: A comparative analysis of Facebook,
Twitter, Delicious, YouTube, and Flickr. Journal
of the Association for Information Science and
Technology, 66(10), 2045-2060.
Park, N., Kee, K. F., & Valenzuela, S. (2009). Being
immersed in social networking environment:
Facebook groups, uses and gratications, and
social outcomes. CyberPsychology & Behavior,
12(6), 729-733.
Park, M., & Lennon, S. J. (2009). Brand name
and promotion in online shopping contexts.
Journal of Fashion Marketing and Management,
13(2), 149-160.
Pavlou, P. A., & Dimoka, A. (2006). e nature
and role of feedback text comments in online
marketplaces: Implications for trust building,
price premiums, and seller differentiation.
Information Systems Research, 17(4), 392-414.
Peters, K., Chen, Y., Kaplan, A. M., Ognibeni,
B., & Pauwels, K. (2013). Social media metrics:
A framework and guidelines for managing social
media. Journal of Interactive Marketing, 27(4),
281-298.
Porter, L., & Golan, G. J. (2006). From subservient
chicken to brawny men: A comparison of viral
advertising to television advertising. Journal of
Interactive Advertising, 6(2), 26-33.
Raja, J. Z., Bourne, D., Gon, K., Çakkol, M.,
& Martinez, V. (2013). Achieving customer
satisfaction through integrated products and
services: An exploratiory study. Journal of Product
Innovation Management, 30(6), 1128-1144.
Ransbotham, S., Kane, G. C., & Lurie, N. H.
(2012). Network characteristics and the value of
collaborative user-generated content. Marketing
Science, 31(3), 387-405.
Rauschnabel, P. A., Praxmarer, S., & Ivens, B.
S. (2012). Social media marketing: How design
features inuence interactions with brand postings
on Facebook. In M. Eisend, T. Langner, & S.
Okazaki (Eds.). Advances in Advertising Research
(Vol. III). Wiesbaden, Germany: Springer Gabler.
Rohm, A., Kaltcheva, V. D., & Milne, G. R.
(2013). A mixed-method approach to examining
brand-consumer interactions driven by social
media. Journal of Research in Interactive Marketing,
7(4), 295-311.
Russell-Bennet, R., & Neale, L. (2009). Social
networking: Investigating the features of Facebook
application. Proceedings of Academy of Marketing
Annual Conference, United Kingdom.
Sabate, F., Berbegal-Mirabent, J., Cañabate, A., &
Lebherz, P. (2014). Factors inuencing popularity
of branded content in Facebook. European
Management Journal, 32(6), 1001-1011.
568
Review of Business Management., São Paulo, Vol. 18, No. 62, p. 545-569, Oct./Dec. 2016
Marcos Inácio Severo de Almeida / Milena Costa / Ricardo Limongi França Coelho / Paulo Roberto Scalco
Schultz, D. E., & Peltier, J. (2013). Social media’s
slippery slope: Challenges, opportunities and
future research directions. Journal of Research in
Interactive Marketing, 7(2), 86-99.
Schulze, C., Schöler, L., & Skiera, B. (2014). Not
all fun and games: Viral marketing for utilitarian
products. Journal of Marketing, 78(1), 1-19.
Smith, A. N., Fischer, E., & Yongjian, C. (2012).
How does brand-related user-generated content
dier across Youtube, Facebook, and Twitter.
Journal of Interactive Marketing, 26(2), 102-113.
Sohn, D. (2009). Disentangling the eects of
social network density on electronic word-of-
mouth (eWOM) intention. Journal of Computer-
Mediated Communication, 14(2), 352-367.
Southgate, D., Westoby, N., & Page, G. (2010).
Creative determinats of viral video viewing.
International Journal of Advertising, 29(3), 349-368.
Stammerjohan, C., Wood, C. M., Chang, Y., &
orson, E. (2005). An empirical investigation of
the interaction between publicity, advertising, and
previous brand attitudes and knowledge. Journal
of Advertising, 34(4), 55-67.
Sun, T., Youn, S., Wu, G., & Kuntaraporn, M.
(2006). Online word-of-mouth (or Mouse): An
exploration of its antecedents and consequences.
Journal of Computer-Mediated Communication,
11(4), 1104-1127.
Swanepoel, C., Lye, A., & Rugimbana, R. (2009).
Virally inspired: A review of the theory of viral
stealth marketing. Australasian Marketing Journal,
17(1), 9-15.
Swani, K., Milne, G., & Brown, B. P. (2013).
Spreading the word through likes on Facebook.
Journal of Research in Interactive Marketing, 7(4),
269-294.
Taylor, D. G., Lewin, J. E., & Strutton, D.
(2011). Friends, fans, and followers: Do ads work
on social networks? Journal of Advertising Research,
51(1), 258-275.
Taylor, D. G., Strutton, D., & ompson, K.
(2012). Self-enhancement as a motivation for
sharing online advertising. Journal of Interactive
Advertising, 12(2), 13-28.
Van der Lans, R., Van Bruggen, G., Eliashberg, J.,
& Wierenga, B. (2010). A viral branching model
for predicting the spread of electronic word of
mouth. Marketing Science, 29(2), 348-365.
Vilpponen, A., Winter, A., & Sundqvist, S.
(2006). Electronic word-of-mouth in online
environments: Exploring referral networks
structure and adoption behavior. Journal of
Interactive Advertising, 6(2), 8-77.
Wellman, B. (2001). Computer networks as social
networks. Science, 293(5537), 2031-2034.
White, H. (1980). A heteroskedasticity-consistent
covariance matrix estimator and a direct test for
heteroskedasticity. Econometrica, 48(4), 817-838.
Wooldridge, J. M. (2013). Introductory
econometrics: A modern approach. Mason, OH:
Cengage Learning.
Yang, J., Yao, C., Ma, W., & Chen, G. (2010). A
study of the spreading scheme of viral marketing
based on a complex network model. Physica A,
389(4), 859-870.
Zarrella, D., & Zarrella, A. (2010). e Facebook
marketing book. Sebastopol, CA: O’Reilly Media.
Zhang, L., Ma, B., & Cartwright, D. K. (2013).
e impact of online user reviews on câmera sales.
European Journal of Marketing, 47(7), 1115-1128.
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Review of Business Management., São Paulo, Vol. 18, No. 62, p. 545-569, Oct./Dec. 2016
“Engage and attract me, then I’ll share you”: an analysis of the impact of post category on viral marketing in a social networking site
About the authors:
1. Marcos Inácio Severo de Almeida, PhD in Management, University of Brasília (UNB), Brazil.
Email: misevero@yahoo.com.br
2. Milena Costa, Bachelor in Management, Federal University of Goiás (UFG), Brazil.
Email: milena_lumus@hotmail.com
3. Ricardo Limongi França Coelho, MSc in Management, Vale do Rio dos Sinos University (Unisinos),
Brazil. Email: ricardolimongi@gmail.com
4. Paulo Roberto Scalco, PhD in Applied Economics, Federal University of Viçosa (UFV), Brazil.
Email: pauloscalco@yahoo.com.br
Contribution of each author:
Contribution Marcos I. S.
Almeida
Milena
Costa
Ricardo L. F.
Coelho
Paulo R.
Scalco
1. Denition of research problem √
2. Development of hypotheses or research questions (empirical
studies) √ √ √ √
3. Development of theoretical propositions (theoretical Work)
4. eoretical foundation/ Literature review √
5. Denition of methodological procedures
6. Data collection
7. Statistical analysis √
8. Analysis and interpretation of data
9. Critical revision of the manuscript √  √
10. Manuscript Writing √ √ √
... The two main streams of research on VDM communication focus on two distinct phenomena: the spontaneous dissemination of messages through eWoM, and the production and dissemination of content under a brand-management strategy. Almeida, Costa, Coelho, and Scalco (2016) categorized studies of these phenomena into four groups: 1) analyses of eWOM communication over various social-media sites. ...
... This reinforced our hypothesis that some users are not suited to instigating the diffusion of messages (Bampo et al., 2008;Kaplan & Haenlein, 2011). This also supports the suggestion of Almeida et al. (2016): that there is a need to adopt carefully designed customer-segmentation strategies if one wishes to engage with consumers. When the three social-media-user types were compared regarding the elements that influence their willingness to share viral content, no significant differences were found except for the impact of the content's meaningfulness on the users' sharing behaviors and attitudes toward the viral content. ...
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Despite the popularity of viral marketing, its influencing factors have not been thoroughly investigated, especially in the context of social media. Successful viral communication depends on the sender's ability to turn receivers into active marketers, which requires the sender to consider both the perspective of the user/recipient and the message content. As the best means of engaging with social-media users and leading them to make a communication viral remains debated, this study seeks to determine the factors that influence users’ willingness to share viral content. In this investigation, partial least squares structural equation modeling was employed to analyze data gathered from an online survey of Facebook users, focusing on a case study of a Facebook event. The results showed that meaningful content affects users’ attitudes regarding sharing communications, and revealed significant differences between user groups regarding the effect the emotional tone and arousal level of content has on sharing behaviors.
... La primera, donde se decide si se considera revisar el contenido y la segunda donde se decide si se interactúa con el mismo. También, Almeida et al. (2016), proponen en su estudio el análisis de diferentes contenidos en una de las más populares redes sociales (Facebook), donde determinaron que existe una relación positiva, entre las categorías marcadas como publicidad y marketing digital. Después de analizar 2583 publicaciones en ocho perfiles de marcas de cerveza brasileñas. ...
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... However, on the Web, a panoply of users can be found; they explore it in different ways by consuming, creating content, participating in discussions, sharing their vision with peers, or simply acquiring information shared by others [6][7][8]. Preferences for content formats vary, leading tourism and hospitality organizations to adopt multiple platform strategies. ...
... The Internet has created a new business paradigm that presents companies with remarkable opportunities and challenges. The Internet has raised new questions about the importance of advertising and what could represent effective marketing strategies in this channel (Almeida, Costa, Coelho, & Scalco, 2016;Hidalgo & Farías, 2016;Nasir, 2017;Zhang & Lin, 2014). ...
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Purpose – This research was intended to analyze the effectiveness of advergames in comparison with banners. In addition, it seeks to analyze whether the type of user (someone who just casually browses the Internet vs. someone looking for product information on the Internet) will influence the effectiveness of both Internet advertising formats. Design/methodology/approach – An experiment with 152 participants controlling perceived entertainment. Findings – it has been observed that advergames are not superior to banners in improving the attitude towards brand and intention to purchase. These results are independent of the type of user (casual browsers vs. information seekers). Originality/value – While banners are traditional formats in Internet advertising, other formats have emerged as technology advances, such as advergames
... Tabela 1 Os procedimentos de sistematização e análise de postagens em redes sociais virtuais vêm sendo realizados com certa frequência por pesquisadores de marketing, que normalmente recorrem a dados de interação no Facebook (Almeida, Costa, Coelho, & Scalco, 2016) ou Twitter (Vargo, 2016). O estudo de Vries, Gensler e Leeflang (2012) Por esses motivos, definiu-se como principais variáveis dependentes do nosso estudo: curtidas, comentários e a marcação a outros indivíduos (boca a boca) nas postagens coletadas no Instagram. ...
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Digital influencers are virtual opinion leaders that represent an alternative for companies targeting propaganda at the community surrounding these individuals. These individuals expand concepts of consolidated theories that examine the process of innovation diffusion and the flow of communication between opinion leaders and followers. Research about the power these opinion leaders have in virtual social networks is still in its early stages and an obvious gap is how to measure the influence these opinion leaders have on engagement. This article details an approach that measures this influence on Instagram by analyzing all posts published in 2015 on four widely followed public profiles. Three hypotheses evaluated differences in engagement metrics provoked by individual (people) and institutional (institutional entities) opinion leaders. The two-stage least squares regression models confirmed the hypotheses that individual opinion leaders intensify engagement: postings on individual profiles produce significantly more likes, comments and word of mouth than posts on institutional profiles. Results from the empirical models are particularly relevant to companies seeking to raise interaction levels with their target audience on virtual social networks.
... Several studies (e.g. Chen, 2016;de Almeida et al., 2016; indicate that content features (systematic cues) play a greater role in retweetability than contextual factors (i.e. heuristic cues). ...
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Purpose To better understand executive communication on social media, the purpose of this paper is to examine the pattern of messages posted by chief executive officers (CEOs) on Twitter and their retweetability (rate of reposting by other users). Design/methodology/approach The study data comprises 1,068 original tweets randomly selected from all Fortune 1000 CEOs’ tweets in 2014. The impact of the contextual factors (industry background, activeness, and Twitter age) and content factors (content types, supplementary information, and linguistic features) on retweetability was examined through regression analyses. Findings CEOs tweet to share information and insights, to promote their companies or products, to update work or life status, and to interact with the public. Original insights, promotional messages, and seasonal greetings were most likely to be retweeted. CEOs’ backgrounds, usage of hashtags, and certainty of language were also positively associated with retweetability. Practical implications CEOs may enhance their online social influence through demonstrating leadership by sharing insights about their organization or industry and posting topical messages (e.g. season’s greetings). Furthermore, CEOs could use hashtags strategically to initiate or participate in discussions and promote their personal visibility. Originality/value This study is one of the first to evaluate how leaders of the largest companies in the USA communicate on Twitter. It contributes to a theoretical understanding of the factors underlying online influence – the influence of the status of the online communicator vs the message content on information forwarding.
... Statistical and scientific study of this field is becoming more common. One recent study used regression analysis and Analysis of Variance testing to understand the factors behind viral social media posts (De Almeida et al., 2016). The authors concluded that the most significant factors behind whether a post is shared or not is (1) if it was created by a fan, and (2) if it contained promotional offers. ...
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This paper explores the determining of which factors/variables, and the optimal levels of these factors that lead toward successful online posts in a B2B context. Using real data from a software development company’s official social media outlets, data made available only to the authors, we conducted a fractional-factorial design with two dependent (output) variables, which were measures of success: number of impressions, and number of actions. We examined the impact of six independent variables (“factors”) and some selected interactions of these factors on the two output measures. The factors are: day of post, time of day of post, presence of an image, presence of a hashtag, length of the message, and specific channel used. Three of the six factors were significant when analyzing number of impressions, while none of the factors made the 5% significance level when analyzing number of actions.
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