Unpacking the social media phenomenon: towards a research agenda

Journal of Public Affairs (2012)
Published online in Wiley Online Library
(www.wileyonlinelibrary.com) DOI: 10.1002/pa.1412

■ Special Issue Paper

Unpacking the social media phenomenon:
towards a research agenda
Jan H. Kietzmann1*, Bruno S. Silvestre2, Ian P. McCarthy1
and Leyland Pitt1
1
2

Beedie School of Business, Simon Fraser University, Vancouver, BC, Canada
Faculty of Business and Economics, University of Winnipeg, Winnipeg, MB, Canada

In this paper, we highlight some of the challenges and opportunities that social media presents to researchers, and
offer relevant theoretical avenues to be explored. To do this, we present a model that unpacks social media by using
a honeycomb of seven functional building blocks. We then examine each of the seven building blocks and, through
appropriate social and socio-technical theories, raise questions that warrant further in-depth research to advance
the conceptualization of social media in public affairs research. Finally, we combine the individual research questions
for each building block back into the honeycomb model to illustrate how the theories in combination provide a
powerful macro-lens for research on social media dynamics. Copyright © 2012 John Wiley & Sons, Ltd.

Social media continues to have a tremendous impact on how people behave online; how they search,
play, converse, form communities, build and maintain relationships; and how they create, tag, modify
and share content across any number of sites and
devices. In response to the ever-increasing penetration rate of social media services and the fierce
competition among new entrants and incumbents,
new business models emerge regularly, where
firms blend unique technologies and business
models to build competitive advantages (Godes
et al., 2005; Godes et al., 2009; Gnyawali et al.,
2010). For instance, so-called ‘freemium models’
that offer basic services for free but advanced
features at a premium, ‘affiliate models’ that drive
consumers to associate websites and Internetadvertising models are all being replaced by new
models that make use of an advanced understanding of monitoring behaviour on social media. For
instance, ‘personalized retargeting’ services follow
and bring back consumers who did not complete
their purchases on a retailer’s site, and ‘promoted
tweets’ allow advertisers to expose regular, personal
tweets (i.e. public, text-based posts of up to 140
characters) to a wider yet highly focused audience.
At the same time, content-sharing sites, microblogs,
*Correspondence to: Jan H. Kietzmann, Beedie School of Business,
Simon Fraser University, 500 Granville Street, Vancouver, BC,
V6C 1W6, Canada.
E-mail: jan_kietzmann@sfu.ca

Copyright © 2012 John Wiley & Sons, Ltd.

social networks, wikis and a plethora of other
consumer-oriented services and platforms continue
to grow.
Of course these interesting social media developments are impacting research, particularly studies
of at the intersection of public affairs and social
media marketing (Terblanche, 2011), online communities (Jones et al., 2004; Bateman et al., 2010),
government activities (Waters and Williams, 2011),
the development of opinion leaders (Crittenden
et al., 2011) or individual customer behaviour
(Thomas, 2004; Hughner et al., 2007; Leskovec
et al., 2007; Büttner and Göritz, 2008; Zhao et al.,
2008; Kaplan and Haenlein, 2010; Kilduff and
Brass, 2010; Neilson, 2010; Ozanne and Ballantine,
2010). In order to provide a managerial foundation
for understanding these new services, consumers
and their specific engagement needs, a honeycomb
framework (Figure 1) was recently presented
(Kietzmann et al., 2011). Its usefulness as a lens
for understanding social media through seven
functional building blocks has since been discussed
widely in scholarly and practitioner-oriented publications. We argue that the same honeycomb model
can serve a very important role for developing
sound research agenda for social media in public
affairs and for identifying and combining appropriate
theoretical lenses.
This honeycomb model helps explain the implications that each block can have for how firms should
engage with social media in three important ways.

J. H. Kietzmann et al.
In fact, each building block of the model presents
an important social media phenomenon that can be
understood using a number of theories. In this
section, we briefly present one theory for every
building block to (i) provide an example of an interesting lens for understanding each building block;
(ii) help build research agenda for social media;
and (iii) illustrate the methodological usefulness of
the honeycomb model for building powerful and
novel combinations of theoretical approaches for
studying social media platforms, consumer engagement, content sharing and community needs.

IDENTITY

Figure 1 Social media functionality

First, the model describes different specific facets
of the social media user experience. Each social
media platform, for instance, is driven by primary,
secondary and tertiary building blocks, which
inform the rationale of important social media
design decisions. Second, using the social media
honeycomb model as an analytical lens allows
managers to conduct a focused a priori study of their
firms’ specific ‘community needs’, whose findings
can then educate the design or use of an appropriate
social media platform. Third, the model can be
used on an ongoing basis, as a lens for monitoring
how dynamic changes of the community’s needs
vis-à-vis changes of the social media tools present
implications for the firm. The seven building blocks
of the model are neither mutually exclusive nor do
they all need to be included in a particular social
media context (Kietzmann et al., 2011). They are
constructs that allow managers and researchers to
understand how different social media functionalities
can be configured.

UNPACKING THE RESEARCH
OPPORTUNITIES OF SOCIAL MEDIA
In this paper, we now re-use the social media honeycomb model and the seven constructs of identity,
presence, relationships, conversations, groups, reputations
and sharing to outline how social media presents
challenges for researchers and to discuss their
usefulness for advancing our understanding of
community relations, corporate social responsibility
and political strategy and marketing, among others.

This functional block describes the extent to which
users choose to reveal their identities in a social
media setting, or alternatively, the degree to which
sites allow or require identities to be shared. Such
identity can be associated not only with elements
such as name, age, gender, profession and location
but also with more subjective information that
reveals users through the conscious or unconscious
‘self-disclosure’ of personal information (Kaplan
and Haenlein, 2010). In this sense, users with their
virtual (re)presentations (Schultze and Leahy, 2009)
share thoughts, feelings, likes and dislikes in an
effort to be understood as the person they desire
or believe themselves to be (Donath, 1999; Swann
et al., 2000; Ma and Agarwal, 2007).
Beyond pragmatic suggestions of how firms
should be aware of the implications of using
identity as part of a social media presence, this
building block offers a wealth of highly interesting
research approaches. Using dramaturgy as an
analogy, Goffman’s (1959) work is a prime example
of how social theory can help understand identity
and motivation behind complex human performances, including of course behaviour in online
communities (Moisio and Arnould, 2005). Goffman
uses a theatre-and-play metaphor to describe how
an interaction is a performance by one actor that
shapes and is shaped by the actor’s environment
and target audience. Part of this performance is the
identity of an actor as the ‘totality of the person’s
thoughts and feelings in reference to oneself as
an object’ (Zhao et al., 2008: 1817). The creation of
identity then reflects a process in which an individual claims an identity that subsequently needs to be
endorsed by others (Zhao et al., 2008).
In an online world, ‘it is possible for people to
reinvent themselves through the production of
new identities’ (Zhao et al., 2008: 1818), in other
words, through claims that cannot easily be validated online. In the social media context, Goffman’s
analogy of the theatre allows researchers to focus
on (i) the various roles single ‘actors’ can play and
the diversity of identities they adopt on different
platforms; and (ii) the choices they make with
J. Public Affairs (2012)
DOI: 10.1002/pa.

Unpacking the social media phenomenon
respect to ‘self-disclosure’ (as it relates to real factual
information) or ‘self presentation’ (which can relate
to fictitious information).
There is a lack of research that investigates
how the same actor manages different identities on
different stages online, for instance, by using a
handle to protect his or her privacy (e.g. on blogs),
‘personal’ identity for self-promotion or staying in
touch with friends (e.g. on Facebook) or by presenting a ‘professional’ identity for self-branding (e.g.
on LinkedIn). As consumers explore more social
media platforms, managing the multitude of different profiles (i.e. partial identities) requires finesse, as
sharing a particular identity or sending messages to
the wrong audience would lead to what Goffman
(1959) calls ‘embarrassments’. As more and more
consumers of social media engage in constant and
conscious role and audience segregation and choose
which elements of their person to reveal, which to
hide and which to embellish, opportunities are
created for using Goffman’s work for research that
yields answers to questions such as the following:
• There is no single identity for a single social media
consumer, as people wear different masks when
they claim their identity for each social media
platform (Zhao et al., 2008). This results in partial
or ‘unreal’ identities becoming endorsed by others.
Beyond single-platform research, how can scholars
of social media understand the highly complex
phenomenon of identity construction and management across platforms?
• Audience segregation breakdowns occur when
different, possibly contradictory, identities of the
same person become known across social media
platforms. How do these ‘embarrassments’ unfold,
and how do others react to the incongruent information about one of their peers? Do they truly lead
to embarrassments, or are they normal occurrences
in the social media context? If they are, how will
this affect our face-to-face interactions and our
design of social media platforms?
• How can researchers study identity, confidentiality
and self-disclosure on non-anonymous sites that
require a high degree of accuracy and information
declaration? How can researchers study identity
and self-presentation on anonymous sites that
permit a high degree of discretion? How can
we separate claims of self-disclosure from selfpresentation? Given the ambiguity and nonaccountability of actors for their identity claims,
how do communities endorse (or not) the identities
created by others? How can social media system
design facilitate this process?

GROUPS
The more ‘social’ one’s network becomes, the bigger
the group of consumers, friends, followers and

contacts. Combined with the growth of social media
offerings, this leads to a tremendous number of
identities online, as people belong to any number
of communities. In this sense, new forms of organizing are required, which are likely not subject to the
same constraints as traditional social structures
(Butler, 2001; Ma and Agarwal, 2007; Schultze and
Orlikowski, 2010). Given this increasingly complex
social media environment, platforms have started
to allow users to organize their contacts into ‘groups’,
which are used for audience segregation and for
circumventing ‘embarrassment’. This building block
represents the extent to which consumers can form
and join communities and sub-communities that can
be open to anyone, closed (approval required) or
secret (invite only); and the degree to which they
can control their membership (Tinson and Ensor,
2001; Godes and Mayzlin, 2004) or even group
moderate the amount of influence some social media
customers or groups exert (Iyengar et al., 2011).
Scholars of social identity argue that behaviour in
groups is different than the collective behaviour of
individuals. In social identity theory, a key argument is that individuals have a number of identities, ranging from highly individual to highly
collective. Tajfel and Turner (1979) posit that it is
the social context that shapes whether an individual
interacts with others more on an individual interpersonal level or a highly collective intergroup
level. One key component of this social context is
group distinctiveness or the rationale by which
individuals set up or join particular groups and
not others. Group members hold their group and
their peers in high esteem, often leading to ingroup
favouritism, and might feel animosity towards
those belonging to other groups.
Applying elements of social identity theory to
social media holds a number of promises. For public
affairs researchers and social media analysts, group
behaviour and the degree to which individuals
participate and engage with the group are still difficult to understand (Forman et al., 2008). Through
introducing a continuum on which group participation ranges from highly individual to highly
collective, social identity theory might help understand the traffic flow and ‘stickiness’ of different
sites. Social identity theory also adds to the understanding of identities by placing them in a wider
social context, and introduces ingroup and outgroup
behaviour (Tajfel, 1974) as an interesting additional
lens for examining the multiplicity of identities and
audience segregation. In social media, how an
individual chooses which platforms and tools to
use and which groups to start or join is often investigated through quantitatively measured network
externalities (Butler, 2001). In other words, the value
of a social media site increases as the number of its
users increases. By investigating how belonging to
a social media platform or group within it leads to
a particularly positive association, social identity
DOI: 10.1002/pa.

theory supports cross-group studies of attitudes and
behaviour within one group (the ingroup) towards
outgroups.
The processes and outcomes of group decision
making have been studied for decades (Wheeler
and Valacich, 1996). Consumer group research
on ethics and environment (Shaw and Clarke,
1999), health (Allsop et al., 2004), fashion (Workman
and Kidd, 2000), food consumption (Hughner et al.,
2007), among others suggest that such groups
are particularly influential through their tremendous communication power. As these often offline
groups continue to be transformed into social media
communities, an organization’s ability to capitalize
on their power hinges on its ability to understand
how these are formed and maintained. Relevant
questions focus on the role of groups and the advantages and dynamics of group creation and membership
could include the following:
• What communication shifts occur during the transformation of offline or online consumer groups to
social media groups? What is the impact on firms,
and what response mechanisms are appropriate?
• Given the complexity of the construct of identity
in social media, what attributes do users consider
when they decide which groups to join? What
are the advantages and disadvantages, from a
member perspective, for the participation in open,
closed or secret groups?
• How is boundary maintenance managed for
social media groups, and by whom? What are
the advantages and disadvantages of belonging
to an ingroup in social media? (How) can these
be managed by existing group support systems?

RELATIONSHIPS
This building block addresses the extent to which
users can be related to other users. We define ‘relate’
as two or more consumers who have some form of
relationship that leads them to converse, share
objects, meet up or simply just list each other as
a friend or fan (Constant et al., 1994; Miranda
and Saunders, 2003). Of course, a discussion of
identity and groups is directly impacted by the
relationships that exist within social media platforms. Especially when individuals have networks
that span hundreds or thousands of members
(followers, friends, etc.), the sorts of relationships
they maintain are important.
Social network theory has been operationalized
to understand relationships in detail in various
contexts, and Kilduff and Brass (2010) highlight
topics including leadership (Pastor et al., 2002),
teams (Reagans et al., 2004), social influence
(Sparrowe and Liden, 2005; Aral, 2010), trust (Ferrin
et al., 2006), power (Brass, 1984), attitude similarity
(Rice and Aydin, 1991) and diversity (Ibarra, 1992),

among others. Structure and flow (Granovetter,
1973; Borgatti and Foster, 2003), two particular
properties from social network theory, help examine, for instance, social media behaviour through
the importance of different relationship traits
(Neilson, 2010). The structural property of relationships refers to users’ social graphs, how many
connections they have and where they are positioned within their network of relationships.
Research shows that users are more likely to be an
influential member (also known as ‘influencers’) in
their network the denser and larger their portfolio
of relationships is, and the more central their
position in the network. The flow property of
user relationships refers to the types of resources
involved in individual relationships and how these
resources are used, exchanged or transformed. It
describes tie strength (Granovetter, 1973), in other
words, the strength of a relationship where strong
tie relationships are ‘long-lasting and affect-laden’
(Krackhardt, 1992: 218) and weak ones are ‘infrequent and distant’ (Hansen, 1999: 84). Granovetter’s
work proposes that strong ties within close intimate
social circles are ineffective compared with weak ties
(i.e. acquaintances) for connecting social networks.
Social network theory’s structural and flow properties were developed long before the emergence of
the Internet, let alone the popularity of social media.
However, the theoretical propositions are highly
relevant for social media firms seeking to define an
appropriate interaction environment for their users,
including such design elements as privacy controls,
message routing, friend introductions and information prioritization (Gilbert and Karahalios, 2009).
On some social media sites, tie strength matters
little, and users can interact in a fairly informally
and without structure (e.g. on blogs). In other
cases, how individual users associate hardly matters
at all (e.g. Twitter and YouTube). Of course, many
sites that rely on highly formal, regulated and structured connections, such as LinkedIn, are often built
around the notion of tie strength and connect users
with their intimate friends, with ‘influencers’ and
with weak ties that will become important peripheral
network members.
Research projects that shed light on the nature of
relationships, the network position of individuals
and the impact of tie strength for their overall
connectivity would be useful for answering such
questions as:
• What are the unintended consequences and the
negative results of tie strength? For instance, how
does false information or ‘embarrassment’ affect
relationships within and across social media networks? How can social media designs circumvent
this?
• What relationship contradictions emerge in ‘multiplex’ relationships; that is, when users are connected by more than one type of relationship
DOI: 10.1002/pa.

(e.g. they are work colleagues and friends)? How
are these managed by social media consumers?
• Are ties in social media the same as ties in the
‘traditional’ online and offline worlds? Given
the diversity of identities, can there be a duality
of strong and weak ties, where one is both an
intimate friend and an acquaintance?

REPUTATION
Reputation is ‘a tool to predict behaviour based
on past actions and characteristics’ (Dingledine
et al., 2003: 1). Because individuals cannot draw
from enough personal historical data for such
predictions, the reputation of other individuals, of
a firm or of a product is a socially shaped opinion
based on aggregate experiences, shared through
word of mouth, coverage in the popular press and
so on (Dellarocas, 2003; Godes and Mayzlin, 2009).
In essence, reputation is about how trust between
parties is developed, assessed and maintained
(Pavlou and Gefen, 2004; Dellarocas, 2005). Relationship management, for firms often pursued
through branding, becomes a conscious act of
linking positive real or perceived perceptions, images
and experiences to a firm or a product. A good
reputation then affects future actions favourably,
perhaps through brand recognition during a purchasing decision. However, this linkage is also
dangerous in the sense that negative associations
affect a reputation adversely.
This construct of reputation and trust (Putnam,
1995; Bourdieu, 2007) has been placed in the context
of online behaviour studies (Büttner and Göritz,
2008; Brunk, 2010). A body of literature discusses
elements that constitute reputation across three dimensions (Habermas and McCarthy, 1985), on the basis
of Plato’s worlds of the Good, the True, and the
Beautiful, where
the enduring respect of the ancient community
would only be accorded those citizens who
served the world of truth in their activities,
showed themselves to be virtuous citizens in the
world of good and also demonstrated the requisite inner and outward grace in the world of the
beautiful (Eisenegger, 2009, p. 12).
In Habermas’ (1985) theory of communicative
action, these are reflected in propositional truth,
normative rightness and subjective truthfulness.
Eisenegger (2009) relates them to separate reputation
dimensions on the basis of an individual’s or firm’s
continuous demonstration of competence (functional
reputation), on whether individuals or firms act
responsibly as good social citizens in accordance to
social norms (social reputation) and is distinctive
enough, with an attractive, even fascinating, profile
capable to inspire others (expressive reputation).
The Internet has added several new dimensions
to this age-old concept (Dellarocas, 2005). In a social

media setting, reputation plays a tremendous role,
particularly on platforms where trust is important
(S. C. Rice, 2011) and ‘trusting beliefs’ need to be
formed quickly (McKnight et al., 2002). However,
because information technologies are not (yet) very
good at determining such highly qualitative criteria,
social media sites rely on ‘mechanical Turks’ to
accumulate word of mouth, that is, tools that
automatically aggregate information originally provided by users to determine trustworthiness and
reputation (Godes and Mayzlin, 2004). In many
cases, although users might seek trust-building
information around functional, social and expressive reputations, this is often only hidden in
data presented quantitatively, through the sheer
numbers of followers for people, view counts for
videos, likes for contents on Facebook, through
ratios and averages of peer ratings, thumbs up
versus thumbs down or through cumbersome
archives of qualitative feedback via endorsements.
Reputation is a complex phenomenon, and social
media often collapses its three dimensions into
simplistic metrics. The development of more
appropriate reputation tools and effective institution-based trust mechanisms (Pavlou and Gefen,
2004) would certainly have an important impact
for platforms built around reputation and trust
between buyers and sellers, involving employers
and employees and among private community
members. Social media service sites are starting to
improve their ability to analyse user-generated
contents from across social media platforms; nonetheless, their effectiveness as ‘tool[s] to predict
behaviour based on past actions and characteristics’
(Dingledine et al., 2003: 1) is limited by the inability
of users to share their experiences appropriately. ‘To
build a good reputation system, we must find
ways to use these data without naively believing
in their quality’ (Dingledine et al., 2003: 3), and questions that investigate how the engagement needs
of a community may inform the improvement of
reputation management systems could include the
following:
• How does the multiplicity of reputations (similar
to identities) take shape in a social media setting,
and how do individuals manage them? How could
new information systems support the management
of this multiplicity?
• How are reputations of virtual entities connected
to real beings? Is there a need for this connectivity
in social media?
• Do the three dimensions of functional, social and
expressive reputations translate into the social media
context? Do their levels of importance change?

PRESENCE
Presence is the extent to which consumers know
if other consumers are accessible at a specific time.
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It may include knowing where others are (in the
virtual or real world); and for instance, whether
they are available, busy or taking a break. In the
virtual world, this happens through status choices
such as ‘available’, ‘hidden’ or ‘idle’. The managerial implication of presence is that firms should
evaluate the relative importance of consumer
availability and location for their businesses. Virtual
presence is directly associated with a desire to
communicate synchronously, engage with others in
real time and have more influential interactions
(Elaluf-Calderwood et al., 2005).
This building block presents interesting challenges
for research. Research on interactivity (Rafaeli, 1988;
Newhagen and Rafaeli, 1996; McMillan and Hwang,
2002; Sundar et al., 2003; Fortin and Dholakia, 2005)
offers interesting insights for presence within social
media contexts. Here, interactivity is defined in terms
of the immediacy of the responsiveness and the
degree to which the communication resembles
human discourse (H. Li et al., 2002). According to
Li et al. (2002), the concept of interactivity has been
investigated within areas such as computer-mediated
communications, Internet and virtual reality, Internet
marketing and advertising, among others. Consistent
with previous literature (e.g. Liu and Shrum, 2002;
McMillan and Hwang, 2002), Sundar et al. (2003)
also highlight the importance of interactivity and
argue that a high interactivity environment gives
consumers considerably more control and choice,
provide a richer sense of feedback and two-way
communication and imbue a greater feeling of
responsiveness and flow.
Despite the fact that presence and interactivity are
intrinsically related, the definition of presence is
controversial and varies within the literature (see,
e.g. Steuer, 1992). Lombard and Snyder-Duch
(2001) argue that presence in the virtual world is
closely related to perception. We draw on Lombard
and Ditton’s (1997) work that states that the various
presence definitions share the central idea of
the ‘perceptual illusion of nonmediation’. For the
authors, the term ‘perceptual’ implies that it is associated with continuous feedback ‘from the human
sensory, cognitive, and affective processing systems’
(Lombard and Ditton, 1997: 10). An ‘illusion of nonmediation’ occurs when a person fails to recognize
the presence of a medium in his or her communication environment (Lombard and Ditton, 1997).
According to the authors, a medium that becomes
invisible (producing the perceptual illusion of
nonmediation) is analogous to an ‘open window’
and can allow a richer social interaction, producing
a sense that there is no border between the ‘two
sides’ of the medium.
Within social media contexts, the perception of
presence can vary depending on the platform
used. For instance, social media platforms such
as Foursquare, MSN, Skype and Trapster allow users
to share their status updates and check-ins. These

platforms enable different levels of presence perception. On the other hand, within social media platforms such as LinkedIn, Facebook and YouTube,
presence is an element that matters less. On the basis
of these considerations, we call for further exploration
of some intriguing research avenues as follows:
• What is the importance of physical or virtual
availability and location for firms within and across
different social media platforms and industry
settings?
• What is the interplay between consumer presence
and interactivity within different social media
platforms?
• What is the implication of interactivity and
‘perceptual illusion of nonmediation’ for a positive
consumer attitude towards a particular technology,
company or offering?

CONVERSATIONS
This block of the honeycomb model is defined as the
extent to which consumers communicate with each
other in social media settings. Many social media
environments are designed to enhance conversations, where consumers come to meet others, find
a job or discover true love or stay abreast of trending topics and new ideas. Although a social media
space where people communicate is of course
very interesting to firms (Constant et al., 1994;
Parent et al., 2011), understanding voluntary knowledge contribution between strangers interacting
through technology-mediated communication (Ma
and Agarwal, 2007) is very difficult. Trying to make
sense of the sheer volume of data that comes out of
the ‘social media firehose’, and knowing when to
participate as a firm, is almost impossible without
appropriate analytical tools and capabilities.
The discourse on environmental velocity within
organizational literature is very promising for
understanding consumer conversations on social
media. Drawing upon research on industrial
dynamics (McCarthy et al., 2010), we argue that
differences in the frequency and direction of a
conversation can have major implications for how
firms monitor and make sense of the ‘conversation
velocity’. The frequency is the number of conversations over a specified period, and the direction
concerns the mood, tone or inclination of the
conversation towards a brand or an issue. If conversations are implemented in a high-velocity
fashion, it means that there is a great amount
of new information readily available each time
(frequency) about a large variety of topics and
opinions (direction). The velocity of conversation is
an indicator that can be used to determine the
extent to which a consumer conversation might go
‘viral’ (Leskovec et al., 2007; Bampo et al., 2008; Aral
and Walker, 2011).
DOI: 10.1002/pa.

There is ample theoretical support for the idea
that word of mouth, or ‘word-of-mouse’, impacts
social media consumers’ actions (Reingen et al.,
1984; Banerjee, 1992, 1993; Bikhchandani et al.,
1992; Foster and Rosenzweig, 1995; Godes and
Mayzlin, 2004). Thomas Jr. argues that the growing
importance of online environments and social
media allowed the emergence of buzz marketing—
‘the amplification of initial marketing efforts by
third parties through their passive or active influence’ (2004: 64). This may exacerbate ‘herding’, a
phenomenon in which individuals make the same
choices influenced by the opinion of others (Van
den Bulte and Lilien, 2001; Mayzlin, 2006; Li and
Hitt, 2008). Van den Bulte and Lilien (2001) find
strong evidence of social contagion and decompose
the adoption process of certain trend into two
different phases: an ‘awareness phase’—in which
social media consumers make sense of the topic
and an ‘evaluation and adoption phase’—in which
they make their decision and adopt (or not) the
new product, service or technology.
Thus, conversation velocity within social media
settings influences collective intelligence, which is
defined as groups of individuals ‘doing’ things
collectively that seem intelligent (O’Reilly, 2007;
Malone et al., 2010). On social media platforms such
as Facebook and Twitter, members establish relationships and conversations that may be embedded
in trust, similar tastes and viewpoints, and other
types of affinity. In a high conversation velocity
environment, consumers evaluate numerous individual inputs (high frequency) on the basis of a
diverse range of topics (discontinuous direction) to
make their individual choices. On the basis of the
aforementioned discussion, the following questions
would help educate the conceptualization of
consumer conversations on social media.
• What are the implications of conversation velocity
(low versus high) to consumer adoption of new
trends, products/services or technologies?
• How should firms entrain their social media
activities to follow and respond to conversations
with different velocities?
• What is the interplay between conversation
velocity and the ‘herding’ phenomenon? How
can information systems design evaluate the
information quality and system quality (McKinney
and Yoon, 2002) on social media?

SHARING
This building block is associated with the extent to
which consumers exchange, distribute and receive
contents (Ozanne and Ballantine, 2010). The term
social often involves exchange of contents and information between people. In many cases, however,
the act of sharing is associated with particular

linkages between people (Engeström, 2001), for
example, favourite movies, similar cars, travels to
the same country and common employers (Berthon
et al., 2008). There are at least three fundamental
managerial implications that the sharing block
offers to firms whose ambition is to engage in social
media: first, there is a need to identify these linkages
or to select new objects that could mediate their
shared interests (e.g. photos for Flickr and videos
for YouTube consumers); second is related to the
degree to which these objects can or should be
shared (e.g. copyright concerns, legality, offensive
or improper contents); and third, what motivates
consumers to share these objects of sociality.
One important theoretical lens to enhance
our understanding of the sharing block of the
honeycomb focuses on either intrinsic or extrinsic
motivation of the users to share contents (Amabile,
1997; Ryan and Deci, 2000; Lakhani and Wolf,
2005). Intrinsic motivation is driven by an intense
interest and involvement in the activity itself,
curiosity, enjoyment, peer recognition, a personal
sense of challenge, accomplishment or belonging,
whereas extrinsic motivation is driven by the desire
to achieve some external reward that is apart
from the activity itself such as money, deadlines,
directives, threats, competition pressure, expected
evaluation, surveillance or job promotion (Amabile,
1997; Ryan and Deci, 2000).
Research on motivation is very rich and has
been used in a number of areas such as education
(Pintrich and Schunk, 2002), work behaviour
(Gagné and Deci, 2005), learning (Cordova and
Lepper, 1996) and performance (Elliott and Dweck,
1988). According to Amabile (1997: 44), ‘although
combinations of intrinsic and extrinsic motivations
are common, one is likely to be primary for a given
person doing a given task’ and as extrinsic motivation increases for a particular activity, intrinsic
motivation must decrease. Deci et al. (1999) corroborate with this idea, arguing that there is a negative
impact of extrinsic rewards on intrinsic motivations,
which may also have an important implication
within social media contexts. In fact, on the basis
of the user-generated content phenomenon, Nov
(2007) argues that to understand what inspires
these social media consumer contributions, it is
necessary to investigate what motivates them to
share contents.
Within social media contexts, motivation may
present a key element to be understood by firms
willing to engage with consumers. The sharing
approach each consumer uses may vary in frequency, or the number of times a consumer shares
contents over a certain period, and intensity, which
is associated with the quality of the contribution in
terms of the time and effort spent (e.g. answering
a simple question at wiki.answers.com would be
different than producing an elaborated video for
YouTube). On the basis of these arguments, sharing
DOI: 10.1002/pa.

within social media contexts may present several
opportunities for further research. Some of the
questions associated with these areas are as follows:
• How and why different motivations (intrinsic or
extrinsic) impel consumers to share objects within
different social media platforms?

• What is the interplay between intrinsic and
extrinsic motivations when consumers share
contents on a particular social media platform?
• What kind of motivation is associated with different levels of frequency (low versus high) and
quality (low versus high) of how social media
consumer share content?

DISCUSSION

Figure 2 Social media lenses

The evolution of Web 2.0 has shifted the power
online, from the static corporate content of the past
to dynamic interaction driven by the active participation of consumers. In a social media setting,
this has lead to human conversations across a
landscape of publishing sites (blogs, wikis), social
networks (Facebook, LinkedIn), mircoblogs (e.g.
Twitter), lifestreams (e.g. FriendFeed), livecasts
(e.g. Justin.tv), social games (e.g. FarmVille) and
content-sharing sites (e.g. YouTube), among others.
With Facebook now being the ‘Number 1’ most
visited website (Leitch, 2011), the popularity of
social media is hard to ignore.
This paper suggests that researchers focus on the
fundamental building blocks of social media to
understand how consumer behaviour is changing.
With this goal, the honeycomb model suggests
identity, presence, relationships, conversations, groups,
reputations and sharing as key constructs for understanding social media and community engagement
needs. In the research context presented here, this
honeycomb model offers four contributions to social
media research and theory:

Figure 3 Research agenda for YouTube and Facebook


DOI: 10.1002/pa.

• Public affairs researchers can use the honeycomb
construct and the seven building blocks as a
starting basis for understanding social media
and are encouraged to expand the model to fit
social media consumer engagement needs,
technological change or their particular research
interests. In this regard, it is not our contention
that the seven building blocks we discuss here
are the only ones, but we recognize them as
highly important ones across a range of social
media services and platforms. For example, for
increased granularity, ‘trust’ and ‘image’ could
become building blocks in addition to ‘reputation’ for a researcher who look specifically at the
intersection of public affairs and marketing on
social media.
• By providing one example of an appropriate
theory for each building block (Figure 2), we
provided an example of an interesting lens for
understanding engagement needs and how these
might be changing over time. By adding three
representative research questions for each building block, we ambition to help build research
agenda for social media. In the spirit of the topic,
it is our hope that this will lead to a public
conversation among researchers about themes
and appropriate theories for social media and
public affairs research.
• Social media and public affairs researchers studying specific social media phenomena might find the
honeycomb model useful for connecting different
research angles, theories and questions related to
their focus. For instance, those concentrating on
changing identities might want to ascertain and
add their own theories and questions for reputation, groups and relationships.
• For social media and public affairs researchers
studying behaviour on individual social media
platforms, the honeycomb model is particularly
useful because most sites have struck a careful
balance among the different building blocks
(Kietzmann et al., 2011). Collecting theories for
each block and then reintegrating them into the
honeycomb can help build powerful and novel
combinations of theoretical lenses for studying
social media platforms, user engagement, content
sharing, community needs and so on. For example, a study of a social network site (e.g. Facebook) could be conducted using a theoretical
combination of how consumers act differently
on different stages (using Goffman’s dramaturgy), and how their position on their social
graph (using Granovetter’s tie strength debate)
is affected by, and affects, the way they communicate (using velocity arguments of McCarthy
et al.), and how consumers perceive the presence
of others and develop trust to each other. For
content-sharing sites (e.g. YouTube) that are
much less about identity, presence and relationship, the theory combination could include lenses

from sharing, conversations, groups and reputation
(Figure 3).
As social media activity will continue to soar,
firms and researchers will increasingly face the challenges of understanding emerging features and
their implications for individuals, communities,
firms and social media platform designers. In this
context, we hope that our paper will generate
greater progress in understanding the symbiotic
relationship between social media consumers and
different social media functionalities. We also hope
that this paper stimulates a discussion of the usefulness of the social media honeycomb model,
the building blocks we decided to emphasize and
the theories we chose as examples. We would
like to see more work on improving the honeycomb model, identifying where appropriate other
functional building blocks and developing new
theoretical lenses for each block to help energize
and direct the much needed agenda for public
affairs research on social media.

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