• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
Innovation Diffusion and Facebook
 

Innovation Diffusion and Facebook

on

  • 10,378 views

A study of how facebook fits in existing innovation diffusion theory.

A study of how facebook fits in existing innovation diffusion theory.

Statistics

Views

Total Views
10,378
Views on SlideShare
10,369
Embed Views
9

Actions

Likes
2
Downloads
341
Comments
0

3 Embeds 9

http://www.lmodules.com 7
https://twitter.com 1
http://www.linkedin.com 1

Accessibility

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    Innovation Diffusion and Facebook Innovation Diffusion and Facebook Document Transcript

    • 
 
 
 
 
 
 
 
 
 
 
 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.

 
 

    • 
 Bibliography
 Barabàsi,
A.‐L.
(2003).
Linked
­
How
everything
is
connected
to
everything
else
and
what
it
 means
for
business,
science
and
everyday
life.
Cambridge,
Massachusetts,
USA:
Plume.
 boyd,
d.
m.,
&
Ellison,
N.
B.
(2008).
Social
Network
Site:
Definition,
History
and
 Scholarship.
Journal
of
Computer­Mediated
Communication
(13),
210‐230.
 Castells,
M.
(2000).
The
Rise
of
the
Network
Society
(Vol.
1).
Oxford,
UK:
Blackwell
 Publishers.
 comScore
World
Metrix.
(2008,
August
12).
Social
Network
Explodes
Worldwide
as
Sites
 Increase
their
Focus
on
Cultural
Relevance.
Retrieved
October
05,
2008,
from
 comScore.Inc:
http://www.comscore.com/press/release.asp?press=2396
 Dijk,
J.
v.
(2006).
Network
Society.
Thousand
Oaks,
California,
USA:
Sage.
 Ellison,
N.
B.,
Steinfield,
C.,
&
Lampe,
C.
(2007).
The
Benefits
of
Facebook
quot;Friends:quot;
 Social
Capital
and
College
Students'
Use
of
Online
Social
Network
Sites.
Journal
of
 Computer­Mediated
Communication
(12),
1143‐1168.
 Facebook.
(2008).
Timeline.
Retrieved
October
7,
2008,
from
Facebook:
 http://www.facebook.com/press/info.php?timeline
 Farrel,
J.,
&
Klemperer,
P.
(2007).
Coordination
and
Lock‐In:
Competition
with
switching
 costs
and
network
effect.
Handbook
of
Industrial
Organization
,
3,
1967‐2072.
 Granovetter,
M.
S.
(1973).
The
strength
of
weak
ties.
The
American
Journal
of
Sociology
,
 78
(6),
1360‐1380.
 Markus,
M.
L.
(1987).
Toward
a
quot;Critical
Massquot;
Theory
of
Interactive
Media:
Universal
 Access,
Interdepence
and
Diffusion.
Communication
Research
,
14
(5),
491‐511.
 Oldach,
S.
(2008,
September).
The
Patent
Scorecard
2008
­
Universities.
Retrieved
 October
10,
2008,
from
Intellectual
Property
Today:
 http://www.iptoday.com/articles/2008‐9‐oldach2.asp
 Rogers,
E.
M.
(2003).
Diffusion
of
Innovations
(4
ed.).
New
York,
USA:
The
Free
Press.
 The
Crimson
Staff.
(2005,
November
13).
Facing
Off
Over
The
Facebook.
Retrieved
 October
13,
2008,
from
The
Harvard
Crimson:
 http://www.thecrimson.com/article.aspx?ref=503343
 Valente,
T.
W.
(1996).
Social
network
thresholds
in
the
diffusion
of
innovations.
Social
 Networks
,
69‐89.
 Watts,
D.
J.
(2003).
Six
Degrees
­
The
science
of
a
connected
age.
New
York,
USA:
Norton.