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Innovation Diffusion and Facebook
1. INNOVATION DIFFUSION and FACEBOOK
‐ criteria to the successful introduction of innovations
by Werner Iucksch
October.2008
2. INTRODUCTION
Network study is a new field for science (Castells, 2000)(Watts, 2003), but
it’s now widely accepted that when nodes (i.e. an individual, in the case
discussed here) are connected in networks (such as social networks), they can
behave very differently than if they are set apart (e.g. people rioting). By
organizing nodes in networks, the system as a whole is very resilient and robust
(Dijk, 2006), but it also makes outcomes of events very hard to predict or to
mould. (Watts, 2003)
However, networks do change. They adopt new information and discard
useless information. The way this process of introduction, adoption and diffusion
happens across social networks has been studied for a long time, but how
individuals (nodes) can influence society (network) only started to become clear
in the early 70s. Much progress has been done since then. As the internet
developed, some web‐based innovations were introduced successfully,
spreading under the measurable environment of data banks, thus enabling social
network scientists to work together with innovation researchers to create fine
tuned theories on how change can be introduced in a network. That is, the study
of how to program the network to adopt a given innovation is advancing.
This paper intends to rely on social network theory and innovation diffusion
models to outline and test, against a real innovation, criteria that are predicted to
be associated with large scale success of a network‐based innovation. It is not the
objective to present an exhaustive list of what it takes to have a successful
innovation, but it is expected that successful innovations show all of the
characteristics. To test it, the paper will discuss whether Facebook, arguably the
3. most successful online innovation in the last 5 years, shows signs of strategic
choices and product developments that suit the criteria.
NETWORK THEORY, INNOVATION DIFFUSION and SUCCESS PREDICTION
Innovation can appear anywhere within a network; however, it is more
likely to appear in the parts that are less strongly connected to the “centre” or
where there is less affinity with established culture. Granovetter (1973) argued
that when looking for a job, it is far more likely to get information about
opportunities by using “weak” ties than with “strong” ones, because the
information someone has access to when using weak ties are more likely to be
different from that the individual already has, in such circumstance, this weak tie
would be a “bridge”. In his words:
“The fewer indirect contacts one has the more encapsulated he will be in
terms of knowledge of the world beyond his own friendship circle”
(Granovetter, 1973, p. 1371)
Similarly, Rogers (2003) writes that although the level of homogeneity
within a group makes the communication (and therefore diffusion) more
effective, “the very nature of diffusion demands at least some degree of
‘heterophily’’ (level of heterogeneity) . Ideally, according to Rogers (2003, p. 19),
to increase the probability of having a successful innovation, the innovator and
the rest of the network would be homophilous in everything, except for the area
of the innovation. This is a balance that is difficult to achieve. Too much
homophily may result into less radical, uninsteresting innovation and too much
4. heterophily may result into highly innovative ideas that a large portion of the
audience simply don’t understand/have high rejection levels.
So as initial three criteria of a successful web‐based innovation:
1) Innovation is likely to appear away from the centre of the network
2) The innovators and the rest of the public are expected to have a degree of
heterophily between them, but ideally only in the key area of the
innovation.
3) The network in which the innovation appears is expected to have a
relatively large number of social clusters weakly connected.
These first few criteria point to where the innovation is expected to be
born and also gives some characteristics of the nodes and actors that are thought
to be present. However, the lives of ordinary people are not majorly constituted
of weak connections and heterophilous communication, thus it is important to
understand how to break with the “pro‐homophily” environment that strong
bonds create.
The work of Watts (2003) about social contagion sheds some light over
this issue. According to the author, social contagion of ideas occurs under very
specific circumstances. Before an innovative idea is adopted widely, it has to
percolate successfully into social clusters. Depending on the connectivity of the
first percolating cluster, it may cascade through to parts of the network or even
the whole network, in what is usually called a global cascade.
The creation of the crucial percolating cluster is a process that is
explained by the concept of critical thresholds. The innovator needs to be
6. Figure 1 – Phase Transition
(based on Watts, 2003, p. 238)
If the innovation is going to be successful, another phenomena should be
observed just after it begins to get adopted outside the immediate boundaries of
its launching cluster. Innovations that are interactive and/or are based on
networks tend to become more useful as the number of users increases, in what
is known as network effect (Farrel & Klemperer, 2007). The number of users will
then increase until a point when the point at which reciprocal behaviour gets
self‐sustainable, that is, gains critical mass (Markus, 1987, p. 496). Once it
happens, the innovation will cascade activating high number “off nodes” very
quickly, soon the thresholds of the whole network will be achieved (figure 2).
7.
Figure 2 – Critical mass
(based on Rogers, 2003, p. 314)
Achieving critical mass early on is important in a competitive market. If a
given innovation can achieve this stage before an alternative concept/innovation
for the same problem establishes itself, it will grow in size and people begin to
gravitate towards the innovation, in what is know as a Power Law, that is “The
rich get richer”. (Barabàsi, 2003). This discourages competitors from entering
the market.
Thus, as final criteria to the purposes of this paper, we have the following
points:
6) The innovation should be able to benefit from network effects, becoming
more relevant as it grow.
8. 7) Successful innovations are likely to be fast into achieving critical mass,
point which it’s growth become exponential.
In the next section of this paper, all these criteria will be put to the test
against what Facebook did to achieve the dominant position it has today.
FACEBOOK
Facebook (www.facebook.com) is a social network site (SNS). According to
boyd & Ellison (2008), SNS’s are “webbased services that allow individuals to (1)
construct a public or semipublic profile within a bounded system, (2) articulate a
list of other users with whom they share a connection and (3) view and traverse
their list of connections and those made by others within the system.”
It was already exposed that it is a very successful innovation, with more than
130 million unique visitors/month (comScore World Metrix, 2008), but the
choice for Facebook go beyond numbers. As Ellison, Steinfield, & Lampe (2007, p.
1144) put it:
“Facebook constitutes a rich site for researchers interested in the
affordances of social networks due to its heavy usage patterns and
technological capacities that bridge online and offline connections.”
Previous social network studies and hard statistics, therefore, suggest
that Facebook’s structure is ideal to a study that intends to bring together
Network Theory and Innovation Diffusion.
9. FACEBOOK AND THE CRITERIA
The first criterion that was outlined is about the location of where
innovation appears. It was predicted that it would appear away from the centre
of the then established culture. Facebook was launched from a student room
inside a university. Not any university, but one of the most innovative
universities in the United States (Oldach, 2008). Although it can hardly be argued
that Harvard is at the margin of the educational system, universities are on the
fringe of society. The innovation was born in an appropriate place if we consider
Facebook was launched 7 years after the first relevant SNS (figure 3). So it was
already competing with the corporate world. Also, it’s was not a formal project
funded by the university, so the marginality of the innovator could be observed.
There are signs that the company actively fought to achieve a balance
between heterophily and homophily. Jones & Soltren (2005) documented that
Facebook, at that time reaching about 2,000 colleges in the USA, wasn’t one
singular website. According to them, “’Facebook’ [was] a collection of sites, each
focused on one of the 2,000 individual colleges.”, with permission of access
restricted to the college/university the user belonged to. This guaranteed
homophilous conditions in many aspects, such as age groups, education level,
even economic background, to a certain extent. The SNS’s founder, Mark
Zuckerberg, was a student at the time of its launch, so he was able to develop the
innovation with an highly intuitive interface to its target, as well as relevant
structure. By doing so, Facebook didn’t face major “noise” in its diffusion. This
was, perhaps, a major early advantage that the company had when compared to
other SNS’s available at that time. The heterophily levels between innovator and
13. CONCLUSION
Based in the literature available about social networks and innovation
diffusion the text outlined critical points that are thought to be present in
successful innovation. When comparing these points to Facebook, a successful
recent innovation, it was possible to see all points described; however, they
didn’t always apply smoothly into the case described.
In at least one occasion, our studied website needed to compromise
between two of the elements the paper was observing (“critical mass” and
“network effects”). Facebook had to chose in its early stages which one of these
elements it wanted to privilege, leaving the other to a second stage of
development.
Although a balance between homophily and heterophily could be seen, it
is difficult to access whether Facebook actually is a strong example of such
delicate balance. It is possible that other, more (or less) innovative SNS’s actually
had a better balance than Facebook, but were eclipsed by Facebook’s success in
other areas (specially in the “speed‐to‐critical mass”, which can be very powerful
in eliminating competitors). The higher degree of heterophily, in this case,
actually resided in the context in which Facebook was launched when compared
to that of other SNS’s, rather than that between innovator and adopters.
These observations and difficulties, however, do not invalidate the
criteria that were selected. The very nature of the a number of the key network
theory concepts that were exposed in this paper require social interactions,
market context, collective decision making and adequate timing to an extent that
14. to date no model is capable of predicting network behaviour with accuracy
(Watts, 2003, p. 29).
Overall, it is possible to say that the criteria selected could be seen as
important to the success of the Facebook, but they may not be necessary in
100% of the cases and sometimes it will be necessary to prioritize one criterion
over another. Such conclusion indicates that companies can probably benefit if
they actively use these criteria in the development of business plans and product
development, but it is important to allow room for change of plans. This way
network theory and innovation diffusion research can help pave the way for
others to also program the network.