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    INNOVATION
DIFFUSION
and
FACEBOOK

       ‐   criteria
to
the
successful
introduction
of
innovations
­


                                                          by
Werner
Iucksch




                                   

                                   


                                   


                                   

                                   

                                   


                                   

                                   

                                   


                                   

                                   

                                   

                                   


                           October.2008

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

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

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

connected
to
people
with
low
adoption
thresholds
to
that
kind
of
innovation,


allowing
for
the
existence
of
early
adopters
(Valente,
1996).

These
first
few


people
can
form
the
initial
percolating
cluster
that
will
be
connected
to
another


individuals,
that
at
this
point
still
didn’t
adopt
the
innovation
(nodes
that
are


“switched
off”).



       The
way
the
initial
cluster
is
connected
to
nodes
that
are
“off”
and
their


level
of
connectivity
to
other
“off”
are
also
crucial.
They
are
as
important
as
the


threshold
of
each
node
in
enabling
their
conversion
and
allow
for
the
cascade
to


happen,
in
a
process
called
phase
transition.

As
we
can
see
in
figure
1,
the
higher


the
number
of
neighbours
an
“off”
node
has,
the
more
difficult
it
is
to
adopt
an


innovation,
thus,
these
nodes
would
need
to
have
a
very
low
threshold
to
adopt
a


given
innovation.
Another
option
would
be
to
try
to
“infect”
nodes
that
have


fewer
connections;
in
this
case
a
higher
threshold
would
be
tolerable.
However,


global
contagion
only
happens
when
connection
level
is
rather
high
(Watts,


2003).





       These
views
further
develops
our
criteria:



    4) People
around
the
innovator
must
have
low
resistance
towards
the


       innovative
idea/product.


    5) The
innovation
must
be
able
to
infect
a
well‐connected
cluster
in
order
to


       spread
globally













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).










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.


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.





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

the
innovation’s
audience
resided
basically
in
the
capacity
to
program
a
social


network
site.
However,
as
SNS’s
were
already
available,
heterophily
was
not
a


distinctive
feature
of
the
website
in
it’s
early
stages.
Once
it
was
opened
to
the


general
public,
in
September
2006,
a
shock
of
cultures
between
students
and


worker/families
might
be
present.
As
this
is
the
case,
the
second
point
outlined


in
our
criteria
could
be
observed
partially.



Figure
3
–
SNS’s
Timeline





                                                                    


                                                      (boyd
&
Ellison,
2008,
p.
212)


At
the
same
time,
by
focusing
in
academic
institutions,
Facebook
became


a
great
alternative
to
many
people
whose
life
stage
carries
a
lot
disruption
with


it.
University
life
many
times
demand
moving
out
of
home,
leaving
friends


behind,
for
instance.
This
way
almost
every
student
has
to
make
new
social


circles,
which
they
do;
however,
bonding
takes
long
to
consolidate.
The
number


of
weak
ties
of
each
student
tends
to
be
very
big
in
these
circumstances,
like


criterion
3
suggests
it
would
be.
Facebook
took
this
into
consideration
and
built


a
variety
of
tools
such
as
“Wall”,
“News
Feed”,
“Photo”
and
“Events”
to
keep
users


informed
about
each
other,
share
moments,
enrich
social
life
without
the
need
to


browse
profiles.

This
way
the
links
do
not
dissolve,
adding
relevance
to
the
SNS


as
it
grew.



       Zuckerberg
certainly
knew
how
valuable
a
system
such
as
Facebook
could


be,
as
he
grew
up
in
a
little
town
around
300km
away
from
Harvard.
Online


profiles,
friends
and
groups
are
convenient
ways
to
(re)build
someone’s
social


capital,
that
is,
the
resources
that
“allow
a
person
to
draw
on
resources
from


other
members
of
the
networks
to
which
he
or
she
belongs”
(Ellison,
Steinfield,
&


Lampe,
2007).
Additionally,
Facebook
helped
to
solve
a
big
problem
in
academic


life:
it
made
easy
to
organize
study
groups,
which
was
something
missing
at


Harvard
(The
Crimson
Staff,
2005)
and
other
universities.
Later
on,
when
the


website
was
already
open
for
the
general
public,
the
launch
of
a
free
platform
to


developers
to
create
applications
allowed
for
introduction
of
tools
that
could
be



relevant
to
very
specific
regions/groups.
All
these
actions
show
that
lowering


threshold
of
adoption
(criterion
5)
was
one
of
the
concerns
of
the
company.

The
initial
structure
of
the
website
and
how
it
expanded
and
developed


are
deeply
related
to
the
last
two
criteria.
By
limiting
access
to
profiles
of
the


school
the
user
belonged
to,
the
company
chose
to
accelerate
the
achievement
of


critical
mass
in
each
one
of
those
schools
independently
to
what
happened
in


other
schools.
At
this
time,
although
the
total
number
of
Facebook
users
grew


exponentially,
network
effects
were
limited
for
it
wasn’t
possible
to
contact


users
from
other
schools.
This
was
a
choice
of
the
company;
it
felt
that
it
was


more
important
to
establish
local
relevancy
before
giving
users
the
chance
of


establishing
long
distance
connections.
In
fact,
this
characteristic
persists
until


today,
as
it
was
found
that
‐
contrary
to
other
SNS’s
‐
Facebook
users
use
it
as
a


complement
to
existing
off‐line
connections
(Ellison,
Steinfield,
&
Lampe,
2007).




       Once
the
base
in
colleges
and
high‐schools
were
solid,
though,
and
the


walls
around
them
were
lifted,
Facebook
grew
as
never
before.
Families
and


friends
that
were
left
behind
when
someone
had
to
university
and
colleagues


that
no
longer
were
to
make
part
of
daily
life
after
graduation
could
now
be


accessed
anywhere
in
the
world.
The
website
jumped
from
5.5
million
active


users,
in
Dec/05,
to
12.5
million,
in
Dec/06
(close
to
the
time
it
opened


registration
to
anyone).
Thirteen
months
after
the
opening,
in
Oct/07,
it
already


had
more
than
50
million
users.
(Facebook,
2008).
Such
statistics
strongly


suggest
that
the
website
and
its
actions
generated
value
to
users
as
more
people


were
joining.










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

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.




       










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A.‐L.
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boyd,
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J.
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