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外文资料翻译
Abstract
This
paper
introduces
the
concept
of
knowledge
networks
toexplain
why
some
business
units
are
able
to
benefit
from
knowledgeresiding
in
other
parts
of
the
c
ompany
while
others
arenot.
The
core
premise
of
this
concept
is
that
a
proper
u
nderstandingof
effective
interunit
knowledge
sharing
in
a
multiunitfirm
requires
aj
oint
consideration
of
relatedness
in
knowledgecontent
among
business
units
and
t
he
network
of
lateral
interunitrelations
that
enables
task
units
to
access
related
k
s
from
a
study
of
120
new
product
developmentprojects
in
41
bu
siness
units
of
a
large
multiunit
electronicscompany
showed
that
project
teams
o
btained
more
existingknowledge
from
other
units
and
completed
their
projects
fas
terto
the
extent
that
they
had
short
interunit
network
paths
to
unitsthat
possessed
related
knowledge.
In
contrast,
neither
networkconnections
nor
extent
of
related
k
nowledge
alone
explainedthe
amount
of
knowledge
obtained
and
project
completi
on
time.
The
results
also
showed
a
contingent
effect
of
having
directinterunit
relations
i
n
knowledge
networks:
While
establisheddirect
relations
mitigated
problems
of
tra
nsferring
noncodifiedknowledge,
they
were
harmful
when
the
knowledge
to
be
tra
nsferredwas
codified,
because
they
were
less
needed
but
stillinvolved
maintenance
costs.
These
findings
suggest
that
researchon
knowledge
transfers
and
synergies
i
n
multiunit
firmsshould
pursue
new
perspectives
that
combine
the
concepts
ofnet
work
connections
and
relatedness
in
knowledge
are
some
business
u
nitsable
to
benefit
from
knowledgeresiding
in
other
parts
of
the
company
while
othersare
not?
Both
strategic
management
and
organization
theoryscholars
have
ex
tensively
researched
this
question,but
differences
in
focus
between
the
various
ap
proacheshave
left
us
with
an
incomplete
understanding
of
whatcauses
knowledge
sharing
to
occur
and
be
beneficialacross
business
units
in
multiunit
firms.
In
one
line
ofresearch,
scholars
have
focused
on
similarity
in
knowledgecontent
among
b
usiness
units,
arguing
that
a
firmand
its
business
units
perform
better
tothe
exten
t
thatunits
possess
related
competencies
that
can
be
used
bymultiple
units
(e.g.,
Rumelt
1974,
Markides
and
Williamson1994,
Farjoun
1998).
While
this
knowledg
e
content
viewhas
demonstrated
the
importance
of
relatedness
in
skillbase,
it
doe
s
not
shed
much
light
on
the
integrative
mechanismsthat
would
allow
one
busine
ss
unit
to
obtainknowledge
from
another
(Ramanujam
and
Varadarajan1989,
Hill
1994).
When
sharing
mechanisms
are
consideredin
this
research,
it
is
often
assu
med
that
the
corporatecenter
is
able
to
identify
and
realize
synergies
arisingfrom
similarity
in
knowledge
content
among
businessunits,
but
this
assumption
is
typic
ally
not
tested
empiricallyand
excludes
a
consideration
of
lateral
interunit
relation
s(Chandler
1994,
Markides
and
Williamson
1994,Farjoun
1998).
In
other
lines
of
research,
in
contrast,
scholars
havedemonstrated
the
importanc
e
of
havinglateral
linkagesamong
organization
subunits
for
effective
knowledgesha
ring
to
occ
ur.
Researchhas
shown
that
a
subunit’sinformation
processing
capacity
is
enhanced
by
lateralinterunit
integration
mechanisms
(e.g.,
Galbraith
1973,1994;
Egelhoff
1993;
Gupta
and
Govindarajan
2000),product
innovation
knowledge
flow
s
more
efficientlythrough
established
relationships
spanning
subunitboundaries
(Tu
shman
1977,
Ghoshal
and
Bartlett
1988,Nobel
and
Birkinshaw
1998,Hansen
199
9),
and
bestpractices
are
transferred
more
easily
when
a
positive
existingrelations
hip
exists
between
the
two
parties
to
atransfer
(Szulanski
1996).
These
lines
of
r
esearch
on
linkageshave,
however,
not
incorporated
opportunities
forknowledge
sh
aring
based
on
commonality
in
knowledgecontent
among
subunits,
but
has
taken
this
aspect
asgiven.
Yet
the
existence
of
both
related
knowledge
in
thefirm
—
i.e.,
expertise
in
the
f
irm’s
business
units
that
canbe
useful
for
tasks
per
formed
in
a
focal
business
un
itand
a
set
of
established
linkages
among
business
unitsseems
necessary
for
inter
unit
knowledge
sharing
to
occurand
be
effective.
In
this
paper,
I
consider
both
d
imensionsand
develop
theconcept
of
task-specific
knowledge
networks
,which
comp
rise
not
only
those
business
units
thathave
related
knowledge
for
a
focal
task
un
it,
but
also
theestablished
direct
and
indirect
interunit
relations
connectingthis
sub
set
of
business
units.
I
define
establishedinterunit
relations
as
regularly
occurring
informal
contactsbet
ween
groups
of
people
from
different
businessunits
in
a
firm,
and
I
assume
thatt
ask
units
will
be
abletouse
these
relations
to
search
for
and
access
knowledgeresi
ding
in
other
business
units.
I
make
two
main
arguments.
First,
with
respect
to
indirect
relations
(i.e.,
conne
ctions
throughintermediaries),I
argue
that
task
teams
in
focal
business
units
with
shortpath
lengths
in
a
knowledge
network
(i.e.,
few
intermediariesare
needed
to
c
onnect
with
other
units)
are
likelyto
obtain
more
knowledge
from
other
business
units
andperform
better
than
those
with
long
path
lengths
becauseof
search
benef
its
accruing
to
business
units
with
shortpath
lengths.
Long
path
lengths,
in
contra
st,
lead
to
informationdistortion
in
the
knowledge
network,
makingsearch
for
usef
ul
knowledge
more
difficult.
Second,
I
arguethat
a
focal
unit’s
direct
established
relations
in
aknowledge
network
are
a
two-edged
sword:
While
theyprovide
im
mediate
access
to
other
business
units
that
possessrelated
knowledge,
they
are
als
o
costly
to
maintain.
They
are,
therefore,
most
effective
when
they
help
teamssolve
difficult
transfer
problems,
as
when
the
knowledgeto
be
transferred
is
noncodified
(Szulanski
1996,
Hansen1999).
Whenthere
is
no
transfer
problem,
they
are
likelyto
be
harmful
fort
ask-unit
effectiveness
because
of
theirmaintenance
costs.
This
knowledge
network
model
seeks
to
advance
ourunderstanding
of
knowled
ge
sharing
in
multiunit
companiesin
several
ways.
First,
by
integrating
the
conce
ptsof
related
knowledge
and
lateral
network
connections
thatenable
knowledge
sha
ring,
the
model
seeks
to
extend
extantresearch
that
has
addressed
only
one
of
th
ese
,
while
extant
research
on
knowledge
transferstends
to
focus
o
n
direct
relations
(i.e.,
the
dyadic
linkbetween
a
recipient
and
a
source
unit
of
k
nowledge),
Ialso
consider
the
larger
organization
context
of
indirect
relations,
which
are
conduits
for
information
about
opportunitiesfor
knowledge
sh
aring
(cf.
Ghoshal
and
Bartlett1990).
This
approach
enables
a
richer
understandin
g
ofsearch
processes
forknowledge
use
in
multiunit
,
while
scholars
of
ten
consider
the
positive
effects
of
network
relations
on
knowledge
sharing,
I
also
considermaintenance
costs
of
n
etworks
byincorporating
thistime
commitment
in
analyzing
the
impact
of
interunit
networkrelations
on
knowledge-sharing
effectiveness
inmultiunit
firms.
Knowledge Networks in
Multiunit Firms
The
joint
consideration
of
related
knowledge
and
lateralinterunit
relations
of
a
knowledge
network
is
illustratedin
Figure
1
for
a
new
product
development
team,
whichis
the
unit
of
analysis
in
this
paper.
Diagram
1a
illustratesa
network
of
re
lations
among
all
business
units
in
a
firm,but
does
not
partition
these
units
into
those
that
have
relatedknowledge
for
the
focal
new
product
developmentteam,
A
(i.e.,
a
pure
network
consideration).
Diagram
1b,in
contrast,
partitions
the
busines
s
units
in
the
firm
intothose
that
have
related
knowledge
for
the
focal
productde
velopment
team
(A)
and
those
that
have
not,
but
thereis
no
consideration
of
then
etwork
among
the
units
(i.e.,a
pure
related
knowledge
consideration).
Diagram
1
illustratesa
project-specific
knowledge
network:
Businessunits
are
partitioned
into
t
hose
that
have
related
knowledgefor
the
focal
product
development
team
(A),
an
d
thecomplete
set
of
network
ofrelations
among
them
are
included,including
both
direct
and
indirect
relations
(i.e.,intermediarylinks
connecting
the
focal
unit
with
othersin
the
knowledge
network).
Both
the
indirect
and
directrelations
affect
the
extent
to
which
a
focal
product
developmentteam
is
able
to
obtain
knowledge
fr
om
otherbusiness
units
and
use
it
to
perform
better.
Effects of Indirect
Relations in Knowledge Networks
A
product
development
team’s
direct
and
indirect
interunitrelations
in
its
know
ledge
network
affect
the
effectivenessof
its
search
for
useful
knowledge
by
being
importantconduits
for
information
about
opportunities
the
existence,
whereabouts,
a
nd
relevance
of
substantiveknowledge
residing
in
other
business
units.
While
busi
nessunits
in
the
network
may
not
be
able
to
pass
onproduct-specific
knowledge
directly,
as
such
knowledgeoften
requires
direct
interaction
with
the
source
to
be
extracted,a
focal
team
that
hears
about
opportunitiesthrough
the
network
can
cont
act
the
source
directly
toobtain
the
knowledge.
Sucknowledge,as
defined
here,incl
udes
product-specific
technical
know-how,
knowledgeabout
technologies
and
mark
ets,
as
well
as
knowledgeembodied
in
existing
solutions,
such
as
already
develop
edhardware
and
software.
Although
direct
relations
in
the
knowledge
networkprovide
immediate
access
a
nd
hence
areespecially
usefulfor
a
focal
team
inquiring
about
opportunities,
indire
ctrelations
are
beneficialas
well,
because
information
aboutopportunities
is
likely
t
o
be
passed
on
by
intermediaryunits
and
eventually
reach
the
focal
team,
provide
d
thatbusiness
units
in
the
knowledge
networkare
reachable.1The
idea
that
interm
ediaries
pass
on
messages
and
thatthey
help
forge
connections
has
been
well
sup
ported
incommunications
and
social
network
research.
Studies
investigatingthe
“s
mall-
world”
phenomenon
demonstratedthat
the
path
length
(i.e.,
the
minimum
nu
mber
of
intermediaries)needed
to
connect
two
strangers
from
differentstates
in
the
United
Stateswas
remarkably
short
and
consistedof
about
five
to
seven
intermedia
ries
(Milgram1967,
Kochen
1989,
Watts
1999).
Early
work
on
innovationresearch
showed
that
new
product
developmentteams
benefited
from
having
a
gatekeeper
o
r
boundaryspanner,
that
is,
a
person
who
scans
and
interprets
theteam’s
environm
ent
and
then
passes
on
information
to
therest
of
the
tea
(Allen
1977,
Katz
and
Tushman
1979).In
social
network
research,
Granovetter
(1973)
showed
that
intermediary
persons
who
are
weakly
tied
to
a
focalperson
are
uniquely
plac
ed
to
pass
on
information
aboutnew
job
opportunities
because
they
are
more
like
ly
thanstrongly
tied
connections
to
possess
nonredundant
information.
The
common
thread
in
these
lines
of
work
is
thatindirect
relations
are
pervasi
ve
conduits
for
ediaries
help
forge
connections
and
pass
on
me
ssagesthat
bridge
two
otherwise
disconnected
actors.
However,
indirect
interunit
relations
may
not
be
perfectconduits
of
informationa
bout
opportunities.
As
informationgets
passed
on
across
people
from
different
uni
ts,there
is
likely
to
be
some
degree
of
imperfect
transmissionof
the
message
abo
ut
opportunities
for
knowledgeuse.
In
particular,
when
information
about
opportun
itieshas
to
be
passed
on
through
many
intermediaries
(i.e.,through
long
paths,
cf.
Freeman
1979),
it
is
likely
to
becomedistorted
(Bartlett
1932,
March
and
Simon
1958).People
who
exchange
such
information
are
prone
to
misunderstandingeach
other,
forgetting
details,
failing
tomention
all
that
they
know
to
others,
filtering,
or
deliberatelywithholding
aspects
of
what
they
know
(Collinsand
Guetzkow
1964
Huberand
Daft
1987,
Gilovich1991).
The
distortion
may
be
unintentional
or
delib
erate(O’Reilly
1978).
Huber
(1982)
relates
a
drama
tic
example,originally
provided
by
Miller
(1972),
of
a
mistakemade
during
the
Vietnam
War.
The
chain
of
mess
ageswas
as
follows:
The
order
from
headquarters
to
the
brigadewas
“on
no
occas
ion
must
hamlets
be
burned
down,”the
brigade
radioed
the
battalion
“do
not
bur
n
down
anyhamlets
unless
you
are
absolutely
convinced
that
the
VietCong
are
in
them;”
the
battalion
radioed
the
infantry
companyat
the
scene
“if
you
think
there
are
any
Viet
Congin
the
hamlet,
burn
it
down
;”
the
company
commanderordered
his
troops
“burn
down
that
hamlet.”
Thus,
themore
intermediaries
needed,
the
hig
her
the
chances
ofsuch
distortion,
and
hence
the
less
precise
is
the
informationth
at
is
passed
on
(Miller
1972,
Huber
1982).
The
implication
of
receiving
imprecise
information
inthis
context
is
that
a
proj
ect
team
cannot
easily
focus
ona
few
opportunities
that
are
especially
relevant,
b
ut
mustinstead
check
anumber
of
imprecise
leads
to
verifywhether
they
are
releva
nt
for
the
team,
resulting
in
a
moreelaborate
interunit
search
process
that
takes
ti
me.
For
example,a
project
manager
in
my
study
told
me
that
he
hadbeen
told
b
y
a
third
party
in
the
company
about
a
groupof
engineer
in
another
unit
who
w
ere
supposed
to
havesome
useful
technical
know-how,
but
when
he
was
ableto
r
each
them
after
trying
for
a
while,
it
turned
out
thatthe
know-how
was
not
relev
ant
for
the
project.
Such
fruitlesssearches
not
only
take
time,
but
also
cause
dela
ys
inthe
project
to
the
extent
that
the
needed
knowledge
inputholds
up
the
comp
letion
of
other
parts
of
he
e
of
the
problem
of
information
distorti
on
whenrelying
on
intermediary
units,
a
focal
team
is
likely
tobenefit
from
short
path
lengths
in
the
knowledge
network(i.e.,
few
intermediaries
required
to
connec
t
a
team
in
afocal
unit
with
other
units).
Short
path
lengths
enable
theteam
to
k
now
about
precisely
described
opportunities
involvingrelated
knowledge
and
allow
it
to
discard
informationabout
irrelevant
opportunities.
The
team
can
thenfocus
on
opportunities
with
a
high
degree
of
realizationpotential
and
can
quickly
contact
p
eople
in
these
unitsand
begin
working
with
them
to
extract
and
incorporatetheir
knowledge
into
the
focal
project.
Thus,
less
time
isspent
evaluating
and
pursuing
opportunities,
reducing
effortsdevoted
to
problemistic
search,
including
search
effo
rtsthat
establish
that
no
useful
opportunities
exist(Cyert
and
March
1992).
Teams
with
short
path
lengthsare
thus
more
likely
than
teams
with
long
path
lengths
to
hear
about
more
opportunities
that
overall
yield
more
usefulknowledge,
to
the
ext
ent
that
opportunities
are
notredundant
to
one
another.
All
else
equal,
this
benefi
tshould
reduce
a
focal
team’s
time
to
complete
t
he
arguments
can
be
summarized
in
two
hypotheses.
HYPOTHESIS
1.
The
shorter
a
team’s
path
lengths
inthe
knowledge
network,
the
more
knowledge
obtainedfrom
other
business
units
by
the
team.
HYPOTHESIS
2.
The
shorter
a
team’s
path
lengths
inthe
knowledge
network,
the
shorter
th
project
completiontime.
Effects of Direct Relations in
Knowledge Networks
The
shortest
possible
path
length
is
to
have
an
establisheddirect
relation
to
all
other
business
units
in
a
knowledgenetwork.
Such
a
network
position
does
not
re
quire
anyintermediary
units
and
should
remove
the
informationdistortion
caused
b
y
using
intermediaries.
However,
unlikeindirect
relations,
which
are
maintained
by
intermediarybusiness
units,
direct
interunit
relations
need
to
bemaintained
by
peo
ple
in
the
focal
business
unit,
possiblyincluding
focal
team
members,
and
require
their
own
setof
activities
that
take
time.
In
the
company
I
studied,
forexample,
product
developers
spent
time
outside
of
theirprojects
traveling
to
other
business
units
on
a
regular
basisto
discuss
technology
developments,
market
opportunities,a
nd
their
respective
product
development
interunit
network
mainten
ancecan
be
adistraction
from
completing
specific
project
tasks:
Timespent
on
mai
ntaining
direct
contacts
is
time
not
spent
oncompleting
project-related
tasks.
Although
direct
interunit
relations
involve
maintenancecosts,
they
also
provide
a
benefit
incertain
situations:Established
direct
relations
between
a
focal
team
and
anotherbusiness
unit
may
be
helpful
when
the
team
identifiesknowledge
that
requ
ires
effort
to
be
moved
from
thesource
unit
and
incorporated
into
the
project.
Fo
r
example,in
a
number
of
projects
in
my
sample,
team
memberswere
frequently
able
to
obtain
software
code
from
engineersin
other
business
units,
but
sometime
s
the
engineerswho
wrote
the
code
needed
to
explain
it
and
help
the
teamto
inc
orporate
the
code
into
the
new
project.
Receivingsuch
help
was
often
much
easie
r
when
the
team
and
theengineers
providing
the
code
knew
each
other
beforehan
d.
This
likely
positive
aspect
of
direct
relations
needsto
be
compared
with
their
maintenance
relations
are
especially
helpful
when
a
team
isexperienci
ng
transfer
difficulties
—
i.e.,
spending
significanttime
extracting,
moving,
and
inco
rporating
knowledgefrom
other
subunits
—
because
the
knowledge
is
noncodified,w
hich
is
defined
as
knowledge
that
is
difficultto
adequately
articulate
in
writing
(Zander
and
Kogut1995,
Hansen
1999).
Relying
on
establisheddirect
relationsmay
ease
the
difficulties
of
transferring
noncodifiedknowledge,
because
the
team
and
people
in
the
directlytied
unit
have
most
likely
worked
with
each
other
beforean
d
have
thus
established
some
heuristics
for
workingtogether,
reducing
the
time
itt
akes
to
explainthe
knowledgeand
understand
one
another
(Uzzi
1997,
Hansen199
9).
When
a
focal
team
experiences
significant
transferdifficulties
because
of
nonc
odified
knowledge,
having
establisheddirect
relations
to
related
business
units
is
li
kelyto
reduce
the
amount
of
time
spent
transferring
knowledge,which
may
offset
the
costs
of
maintaining
such
relationsand
shortening
project
completion
time.
In
particular,having
a
number
of
direct
relations
in
a
knowledgenetwork
increases
th
e
likelihood
that
a
team
will
be
ableto
use
one
of
them
in
transferring
noncodifi
ed
knowledge.
Thus,
while
indirect
relations
are
beneficial
to
the
extentthat
they
serve
as
inte
rmediaries
that
provide
a
focal
unitwith
nonredundant
information,
direct
relations
are
beneficialto
transferring
noncodified
knowledge,
implyingthat
the
benefit
of
ha
ving
intermediaries
supplying
nonredundantinformation
is
relative
(cf.
Burt
1992).I
n
contrast,
this
transfer
benefit
of
direct
relations
isless
important
when
a
focal
t
eam
can
easily
extract
andincorporate
the
knowledge
that
was
identified
in
anoth
ersubunit,
as
when
that
knowledge
is
highly
codified.
Inthese
situations,
direct
int
erunit
relations
are
not
usefulfor
transfer,
but
they
still
carry
maintenance
costsw
hichtake
time
away
from
the
completion
of
the
project
to
theextent
that
team
me
mbers
d
not
have
slack
resources
thatcan
be
devoted
to
maintaining
these
relatio
nships.
Themore
suchrelations
that
are
maintained
by
a
focal
unit,the
higher
the
maintenance
costs,
and
the
more
time
istaken
away
from
completing
a
project.
T
he
arguments
canbe
summarized
as
follows:
HYPOTHESIS
3A.
The
higher
a
team’s
number
of
directrelations
in
the
know
ledge
network,
the
shorter
the
projectcompletion
time
when
the
knowledge
to
be
transferredis
noncodified.
HYPOTHESIS
3B.
The
higher
a
team’s
number
of
directrelations
in
the
knowl
edge
network,
the
longer
the
projectcompletion
time
when
the
knowledge
to
be
tr
ansferred
iscodified.
Data and Methods
Setting
I
tested
the
knowledge
network
model
in
a
large,
multidivisionaland
multinatio
nal
electronics
company
(hereaftercalled
“the
Company”).
I
negotiated
access
to
t
hecompany
through
three
senior
corporate
R&D
managersand
initially
visited
14
divisions
where
I
conducted
openendedinterviews
with
50
project
engineers
and
managersto
better
understand
the
context,
and
todevelop
surveyinstruments.
The
c
ompany,
which
has
annual
sales
ofmore
than
$$5
billion,
is
involved
in
developin
g,
manufacturing,and
selling
a
range
of
industrial
and
consumerelectronics
produc
ts
and
systems,
and
is
structured
into41
fairly
autonomous
operating
divisions
tha
t
are
responsiblefor
product
development,
manufacturing,
and
sales.
By
focusing
on
these
divisions,
I
was
able
to
compareunits
that
occupy
the
sa
me
formal
position
in
the
Company,thereby
controlling
for
a
potential
source
of
variationin
formal
structure.
They
all
had
the
same
formalstatus
as
a
business
uni
t
with
profit-and-loss
responsibility,all
had
a
general
manager,
and
none
of
the
di
visionsreported
to
another
division.
In
additio
to
interunit
relations,there
were
a
f
ew
other
integrative
mechanismsacross
divisions,
notably
divisionwide
conferences
andelectronic
knowledge
management
systems,
but
initial
interviews
revealed
that
these
did
not
vary
much
among
thedivisions.
Selecting
Product Development Projects
I
used
two
surveys:
a
network
survey
administered
to
theR&D
managers
in
th
e
41
divisions
and
a
survey
for
theproject
managers
of
the
product
development
projects
included
in
this
study.
In
selecting
projects,
I
first
createda
list
of
all
projects
that
the
di
visions
had
undertaken
duringthe
three-year
period
prior
to
the
time
of
data
colle
ction.I
then
excluded
very
small
projects
(i.e.,
those
withless
than
two
project
en
gineers)
and
projects
that
had
not
yet
moved
from
the
investigation
to
the
developmentphase
and
were
therefore
ha
rd
to
track
I
also
excludedidiosyncratic
projects
that
had
no
meaningful
start
and
end
(e.g.,
special
ongoing
customer
projects).
Includingonly
successfully
complete
d
projects
may
lead
to
an
overrepresentationof
successful
projects,
biasing
the
res
ults.
I
therefore
included
both
canceled
projects
and
projectsstill
in
progress.
After
having
removed
too-small,
premature,and
idiosyncratic
projects,
I
ended
up
with
a
listof
147
projects.
The
project
managers
of
120
of
thesereturned
their
survey,
yielding
a
response
rate
of
85%.
Ofthe
120
projects,
22
were
still
in
progress
at
the
time
ofdata
collection,
four
had
been
canceled,
and
54
reporteda
significant
t
ransfer
event
involving
another
division.
Specifying
Project-Specific Knowledge Networks
Identifying
Related
Subunits.
Together
with
the
threecorporate
R&D
managers,
I
developed
a
list
of
22
technicalcompetencies
that
constituted
related
knowledgea
reas(see
Appendix
1
for
the
list
of
technical
competencies).2
I
asked
the
R&D
managers
in
the
divisions
to
indicate
up
to
four
specific
competencies
of
their
divisionson
this
list
and
to
add
any
if
they
thought
the
listwas
incomplete.
The
three
corporate
R&D
managers
r
eviewedthe
responses
to
verify
whether
it
made
sense
togroup
those
divisions
tha
t
had
reported
the
same
project
managers
of
the
120
projects
we
rethen
asked
to
indicate
what
technical
competencies
thespecific
project
required
and
were
presented
with
thesame
list
that
was
presented
to
the
divisional
R&D
managers.
Thus,
for
a
given
project,
a
number
of
divisionshad
a
competence
that
matche
d
the
requirements
listedby
the
project
manager
(see
Appendix
1
for
the
distribut
ionof
projects
per
competence).
For
example,
a
projectmanager
indicated
that
his
project
required
technicalcompetencies
in
three
areas:
distributed
measurement,com
munication
system
monitoring,
and
optics.
Twelve
different
divisions
had
at
least
one
of
these
technical
competenciesand
thus
constituted
theknowledge
network
fo
rthis
particular
project.
Specifying
Interunit
Relations.
A
group
of
engineers
ina
di
vision
typically
maintained
an
informal
regular
contactwith
a
group
of
engineers
inanother
division,
and
aproject
team
would
use
such
contacts
to
access
other
di
relationships
were
common
knowledge
inthat
most
product
develope
rs
seemed
to
know
about
theirexistence
and
how
to
use
them,
and
I
was
told
in
preliminaryinterviews
that
a
main
responsibility
of
a
division’sman
agers
was
to
p
rovide
these
contacts
for
his
or
herproject
teams,should
the
need
arise.
I
therefor
e
assumedthat
at
least
one
member
of
a
project
team
woul
knowabout
the
divisi
onal-level
contacts
and
that
the
teammembers
could
access
these
contacts
if
they
wanted
e
of
the
importance
of
these
interdivisional
contactsin
the
compa
ny,
I
chose
to
focus
on
these
types
ing
previous
research,
I
use
d
a
key
informant
toobtain
information
on
interdivisional
relations
(Knokeand
Ku
klinski
1982,
Marsden
1990).
I
considered
the
divisionalR&D
managers
to
be
the
most
appropriate
informantsbecause
they
were
“in
the
thick
of
things”
in
theR&
D
department
in
their
division.
The
R&D
manager
ineach
of
the
41
divisions
re
ceived
a
questionnaire
asking,“Over
the
past
two
years,
are
there
any
divisions
fr
omwhom
your
division
regularly
sought
technical
and/ormarket-
related
input?”3
T
he
question
was
followed
by
alist
of
the
41
divisions
included
in
the
study,
allo
wingrespondents
to
indicate
whether
they
had
a
tie
to
any
onthe
list,
leading
to
a
complete
network
where
everybodywas
asked
whether
a
tie
existed
with
everyb
ody
else(Marsden
1990).
Because
I
asked
everybody
to
indicatewhether
a
tie
exis
ted
with
each
of
the
other
40
divisions,I
avoided
a
potential
bias
resulting
from
having
to
asksomeone
to
ascertain
whether
ties
exist
among
others(Krackhardt
an
d
Kilduff
1999).
To
validate
the
responses,
I
employed
the
crossvalidationmethod
used
by
Krac
khardt
(1990by
askingthe
R&D
managers
who
comes
to
them
for
input.
Anactual
tie
exists
when
both
divisions
agree
that
one
comesto
the
other
for
input.
I
then
sent
an
e-mail
to
all
of
theR&D
managers,
asking
them
about
the
ones
about
w
hichthere
was
no
joint
agreement.
On
the
basis
of
their
responses,I
included
som
e
of
these
suspect
ties
and
excludedothers.
Merging
Network
and
Project
Data.
I
constructedproject-specific
knowledge
net
works
by
including
all
relationsamong
divisions
possessing
related
knowledge
for
a
given
project.
For
example,
for
the
aforementioned
projectfor
which
there
were
12
related
divisions,
I
includedall
relations
among
these
12
divisions,
and
this
ne
tworkconstituted
the
project-specific
knowledge
network.
Toconstruct
these
project
-specific
networks,
I
merged
theproject
data
with
the
divisional
network
data
by
ass
igninga
division’s
network
relations
to
its
projects.
Thus,
interdivisionalties
bec
ame
the
equivalent
of
interdivisionalproject
ties.
It
is
important
to
record
thevalu
es
on
thenetwork
variables
prior
to
the
start
of
a
project
becausemy
theoretical
a
rguments
assume
that
a
project
team
usesestablished
preexisting
interunit
ties
to
s
earch
for
andtransfer
knowledge.
Following
the
approach
of
Burt(1992)
and
Podo
lny
and
Baron
(1997),
I
handled
this
issueby
measuring
the
interdivisional
netwo
rk
relationsover
several
years
by
only
assigning
network
ties
thatexisted
prior
to
the
start
of
the
project.
This
proceduregenerated
time-
varying
network
data
from
informationthat
the
respondents
could
recall.
The
potential
bias
in
this
approach
is
that
it
may
excludesome
relations
that
e
xisted
prior
to
a
project’s
startbut
that
ceased
to
exist
by
the
time
the
R&D
ma
nagerscompleted
the
survey.
This
problem
can
be
partially
controlledfor.
This
pot
ential
bias
should
be
more
of
a
problemfor
projects
in
divisions
in
which
relatio
ns
come
andgo
than
in
divisions
with
long-
lasting
relations.
If
a
division’srelatio
ns
are
long
lasting,
then
it
is
less
likelythat
there
were
some
relations
that
cease
d
to
exist
betweenthe
time
just
prior
to
the
project’s
start
and
the
time
of
surveying.
To
control
for
this
potential
bias,
I
entereda
control
variable
for
th
e
average
age
of
direct
relationsto
related
subunits
(
Age
relations
).
Dependent Variables
Project
Completion
Time.
To
assess
project
task
performance,I
measured
projec
t
completion
time
as
thenumber
of
months
from
the
start
of
concept
developmen
tto
the
time
of
marketintroduction
for
a
given
project
(ortime
to
the
end
of
the
study
period
or
cancellation
forongoing
and
canceled
projects,
respectively).
I
def
inedstarting
time
as
the
month
when
a
dedicated
personstarted
working
part
or
f
ull
time
on
the
project,
whichtypically
coincided
with
the
time
an
account
was
o
penedfor
the
project.
I
defined
the
end
date
as
the
date
on
which
the
product
was
released
to
shipment,
which
is
a
formalmilestone
date
in
this
co
mpany
because
it
signifies
thatthe
product
is
ready
to
be
manufactured
and
shipp
ed
ona
regular
basis.
These
definitions
turned
out
to
be
veryclear
and
provided
f
ew
problems
in
specifying
the
start
and
completion
times,
which
were
14.8
months
on
averagefor
completed
projects.
Scholars
have
proposed
two
alternative
measures
ofcompletion
time.
First,
com
pletion
timecan
be
measuredas
the
extent
to
which
the
project
is
finished
on
sch
edule(e.g.,
Ancona
andCaldwell
1992).
The
assumption
in
thisschedule
measure
is
that
inherent
project
differences
areaccounted
for
in
the
original
schedule,
but
als
o
that
everybodysets
equally
ambitious
schedules,
which
was
mostlikely
not
true
in
this
company,
where
individual
projectmanagers
set
their
own
targets.
A
secon
d
approach
is
togroup
projects
according
to
some
similarity
measure
andthen
tak
e
a
project’s
deviation
from
the
mean
completiontime
of
the
group
(Eisenhardtan
d
Tabrizi
1995).
Theproblem
with
this
approach
is
that
the
mean
deviationrelies
on
a
clearsimilarity
measure
that
was
not
easy
toattain
in
this
setting.
Given
that
these
two
alternativemethods
seemed
problematic,
I
chose
to
use
the
numberof
m
onths
as
the
dependent
variableand
then
add
projectspecificvariables
to
control
for
inherent
differences
betweenthe
projects.
Amount
Acquired
Knowledge.
During
field
interviewsI
was
told
that
the
most
c
ommon
knowledge
that
projectteams
received
from
other
divisions
took
the
form
of
technicalsolutions
embodied
in
already
developed
softwarecode
and
hardware
components.
T
here
were
two
types
of“ware”
being
used
in
the
projects—
standar
input
to
theproducts
being
made
(e.g.,
components
that
were
used
innearly
all
os
cilloscopes
being
manufactured),
and
warethat
helped
solve
ad
hoc
problems
that
were
unique
to
agiven
project
(i.e.,
technical
know-how
that
had
been
embodiedi
n
software
code
or
hardware).
While
the
formerwas
typically
handled
within
divi
sions,
the
latter
was
typicallyobtained
through
interdivisional
network
ause
my
theoretical
analysis
focuses
on
knowledgethat
was
obtained
to
solve
ad
hoc
problems
for
a
project,I
chose
to
focus
on
software
and
hardware
that
the
f
ocalteam
obtained
from
other
divisions
to
solve
emergingproblems.
With
a
few
e
xceptions,
most
of
the
ware
obtainedfrom
other
divisions
was
of
this
kind.4
Duri
ng
pretests,project
managers
thought
they
could
indicate
theamount
of
ware
obtai
ned
from
other
divisions
fairly
accurately.
The
project
manager
was
asked
to
indicate
thepercentage
of
all
the
project’s
s
oftware
andhardware
thatcame
from
other
divisions
in
the
company
(see
Appendi
x1
for
the
specific
question).
To
construct
the
dependentvariable,
I
computed
thef
raction
of
ware
(ranging
from
zero
to
one)
that
came
from
other
divisions
(
Amount
acquiredknowledge
).While
e
ngineers
also
obtained
other
types
of
knowledgefrom
other
divisions,
such
as
inf
ormal
technical
advice
notembodied
in
either
software
or
hardware,
these
were
m
oredifficult
to
quantify,
and
I
therefore
did
not
develop
a
separatedependent
varia
ble
for
these
types.
However,
I
did
askthe
project
manager
to
indicate
the
extent
to
which
theteam
had
obtained
such
knowledge,
and
thismeasure
correlated0.7
wi
th
the
chosen
dependent
variable.
It
is
thuslikely
that
my
measure
of
amount
acq
uired
knowledge
isa
proxy
for
more
informal
types
of
knowledge
obtainedthroug
h
the
network
in
this
setting.
Independent Variables
Path
Lengths
in
a
Knowledge
Network.
I
relied
on
geodesicsto
compute
the
di
stances
in
the
network.
A
geodesicis
the
shortest
path
length
(i.e.,
the
one
with
fewestintermediaries)
between
a
focal
division
and
another
divisionin
a
knowledg
e
network
(Wasserman
and
Faust1994).
However,
the
measure
is
complicated
bec
ause
severalof
the
project-specific
knowledge
networks
were
disconnectedin
that
s
ome
divisions
did
not
have
a
tie
withother
divisions
in
the
knowledge
network.
I
handled
thisproblem
by
creating
a
control
variable
that
indicates
the
fraction
of
related
divisions
that
were
reachable
in
aknowledge
network
(
Reach
).
This
variable
takes
on
avalue
of
zero
if
no
divisions
were
reachable
(i.e.,
therew
ere
no
paths
connecting
the
divisions)
and
a
value
ofone
if
all
divisions
in
the
project-specific
knowledge
networkwere
reachable
(the
mean
value
for
this
variabl
e
is0.85).I
used
the
measure
of
closeness
centrality
to
measurepath
lengths
in
the
network
(Freeman
1979).
Closenesswas
measured
as
(Wasserman
and
Faust
1994)
where
d
(
ni
,
nj
)
is
the
geodesics
linking
divisions
ni
and
nj
.Summing
over
all
r
eachable
related
divisions
excludingthe
focal
one
(
g
_
1),
this
gives
division
ni
’s
total
closenessscore.
Thimeasure
is
standardized,
so
that
a
divisionhas
the
shorte
st
path
length
(i.e.,
is
closest)
to
relateddivisions
when
the
index
is
one
and
the
longest
pathlength
when
the
index
is
near
zero
(
Close
related
).
Thesemeasures
w
ere
computed
in
UCINET
IV
(Borgatti
et
al.1992).
Direct
Relations
with
Divisions
in
a
Knowledge
Network.
Because
direct
relation
s
were
asymmetric
in
thenetwork
in
the
Company,
I
distinguished
between
direct
relations
in
which
the
focal
team
went
to
other
divisionsfor
advice
(i.e.,
advice-
s
eeking
relations)
and
direct
relationin
which
other
divisions
went
to
the
focal
on
e
foradvice
(i.e.,
advice-
giving
relations).
Each
type
of
relationsimplies
different
c
osts.
Advice-
seeking
relationsneed
to
be
maintained,
while
advice-giving
relations
requiretime
helping
others.
I
coded
the
number
of
directadvice-
seeking
relations
to
related
divisions
by
countingthe
number
of
preexisting
divisional
ties
to
divisi
ons
thathad
related
knowledge
for
a
project
and
then
assigned
thatvalue
to
the
f
ocal
project
(
Outdegree
related
).
I
thencoded
the
number
of
direct
advice-
giving
relations
to
relateddivisionsby
counting
the
number
of
preexisting
divisionalties
in
which
a
related
division
reportedly
wentto
the
focal
division
for
advice
on
a
reg
ular
basis
(
Indegreerelated
).
To
control
for
the
possibility
that
these
variables
aresimply
an
indication
of
th
e
division’soverall
number
ofdirect
relations,
I
als
o
included
similar
measures
for
directrelations
outside
a
project’s
knowledge
network.
I
subtractedthe
number
of
r
elated
advice-seeking
ties
from
thetotal
number
of
direct
advice-seeking
relations
for
the
focaldivision
to
arrive
at
the
unrelated
advice-seeking
ties(
Outdegree
unrel
ated
).
I
subtracted
the
number
of
relatedadvice-giving
ties
from
the
total
number
of
advice-
givingties
to
compute
the
number
of
unrelated
advice-givingties
(
Indegr
ee
unrelated
)
.Finally,
I
included
a
measure
of
the
strength
of
relatedadvice-seeking
ties.
Pre
vious
research
has
shown
thatweak
ties
may
facilitate
search
but
impede
the
tran
sfer
ofcomplex
knowledge
(Hansen
1999).
Although
the
theoryin
this
paper
does
not
pertain
to
the
effects
of
tie
weaknesson
interunit
knowledge
transfers,
I
want
ed
to
control
forthe
possible
effect
of
tie
weakness.
Tie
weakness
wascomputed
by
asking
the
R&D
divisional
managers
to
indicateon
a
seven-point
scale
how
fr
equently
people
intheir
division
talked
to
people
in
the
other
division
andhow
cl
ose
their
working
relationship
was
(see
Appendix1
for
the
specific
questions).
I
t
ook
the
average
frequencyand
closeness
for
related
advice-seeking
ties
to
comput
ethe
measure
(
Strength
related
).
Noncodif
ied
Knowledge.
I
constructed
a
three-item
scale
of
noncodification
(see
Appendix
1
for
the
specificitems)
and
asked
the
pro
ject
manager
to
indicate
the
level
of
codification
of
the
knowledge
that
the
project
teamreceived
from
other
divisio
ns
(
Noncodified
).
This
variablewas
then
interacted
with
the
number
of
relatedadvi
ce-
seeking
relations
to
test
the
hypothesis
(
Noncodified
_
outdegree
related
).
Alternative
Explanations.
I
included
variables
to
tesfor
the
possibility
that
eithe
r
short
pat
lengths
or
relatedknowledge
(but
not
both)
explains
the
amount
of
ac
quiredknowledge
and
product
development
time.
First,
I
includedan
overall
closen
ess
centrality
measure
by
usingthe
above
equations
for
the
closeness
centrality
m
easure,using
the
entire
set
of
41
divisions
as
he
relevant
network(
Close
all
).
To
make
this
analysis
comparable
to
the
restof
the
analysis,
I
also
included
a
variable
indicating
thetotal
number
of
direct
advice-
seeking
relations
(
Outdegreeal
l
).
If
the
estimate
for
the
general
closeness
measureis
positive
and
significant,
th
en
thenetwork
argumentabout
the
importance
of
close
positions
(irrespective
ofkn
owledge
relatedness)
is
plausible.
To
capture
the
extentof
related
knowledge
avail
able
to
a
project
team,
I
includeda
variable
measuring
thenumber
of
related
divis
ions(
No.
related
units
).
If
this
measure
of
the
extent
ofrelated
knowledge
in
the
Company
is
positive
and
significant,then
the
argument
about
the
importance
of
re
latedknowledge
(irrespective
of
network
relations)
isplausible.
Control
Variables
Betweenness
Centrality.
Because
the
closeness
centralitymeasures
may
be
correl
ated
with
other
centrality
measuresthat
attempt
to
capture
other
causal
mechanism
s,
included
a
measure
of
betweenness
centrality,
which
isoften
used
to
measure
a
focal
actor’s
brokering
positionin
the
network
(Freeman
1979,
Brass
and
Burkhar
dt1992,
Burt
1992).
Divisions
with
high
betweenness
maybe
in
a
powerful
positi
on
where
they
can
control
the
flowof
information
betweentwo
other
units,
thus
u
sing
thisbenefit
to
obtain
favors
from
others,
such
as
help
in
transferringknowled
ge.
To
control
for
this
power-orientedbenefit
of
central
positions,
I
included
a
m
easure
of
betweennesscentrality
(Wasserman
and
Faust
1994)where
gjk
is
the
nu
mber
of
geodisics
linking
division
j
and
k
,
and
gjk
(
ni
)
is
the
number
of
geodisic
s
linking
division
j
and
k
that
involve
the
focal
division
i
.
The
measure
is
asum
of
the
probabilities
that
the
focal
division
will
fallon
the
geodesics
linking
all
pa
irs
of
related
divisions.
Themeasure
is
standardized
as
follows:where
the
denomin
ator
is
the
number
of
pairs
of
divisionsnot
including
the
focal
division
i
.
This
m
easure
rangesfrom
zero
to
one,
where
one
is
the
maximum
related
betweennessa
mong
related
divisions
(
Between
related
).
Project
Attribute
Controls.
To
make
the
projects
comparable,I
controlled
for
se
veral
project-
specific
factors.
Icontrolled
for
the
extent
to
which
the
project
useds
oftwarecontrols
to
soe
extent
for
a
project
team’s
motiva
tionto
conduct
searches
t
hrough
the
interunit
team
should
be
less
motivated
to
the
extent
tha
t
itcan
use
existing
ware
inside
its
owndivision.
Projectmanagers
were
asked
to
i
ndicate
the
percentage
of
allsoftware
and
hardware
in
theproject
that
they
reused
orleveraged
from
their
own
division
(
Own
existing
ware
).
I
used
the
log
of
estimated
dollar
costs
at
the
start
ofthe
project
to
control
fo
r
size
and
scope
differences
betweenthe
projects
(
Budget
).5
In
my
field
interview
s
withproject
managers,
I
was
also
told
that
estimated
costs
captureinherent
differ
ences
in
technical
complexity
amongthe
projects
(the
more
complex
the
technolog
y,
the
moreengineering
hours
billed
to
the
project).
I
used
the
budgetfigure
to
av
oid
an
interaction
between
final
costs
and
thedependent
variable.
High
final
costs
may
reflect
longcompletion
time
because
of
more
engineering
hoursbilled
to
the
project.
I
also
coded
whether
a
project-specific
patent
was
appliedfor,
to
measure
degre
e
of
innovation
(
Patent
),
andwhether
the
project
team
developed
a
product
or
a
system(
Product
).
More
innovative
projects
presumably
takelonger
to
complete.
Th
e
product-systems
distinction
was
entered
as
a
variable
to
control
for
possible
differencesbetween
these
two
categor
ies
with
respect
to
crossdivisionalknowledge
use.
Each
variable
was
coded
as
adu
mmy
variable,
where
avalue
of
one
indicates
a
patentand
a
product,
respectively.
Finally,
because
strictly
personal
relations
spanningsubunits
may
be
used
by
te
am
membersto
obtain
knowledge,I
entered
a
control
measure
that
was
obtained
fr
oma
third
survey
that
was
sent
to
all
engineers
on
the
projectsin
the
sample
(see
Hansen
et
al.
2001).
Engineers
wereasked
to
indicate
the
number
of
advice-
seeki
ng
relationsthat
they
personally
had
to
people
in
other
divisions.
Ithen
summed
t
hese
relations
for
a
team
(excluding
contactnames
mentioned
more
than
once)
toa
rrive
at
a
teamlevelmeasure
of
direct
interpersonal
relations
spanningsubunits
(
Per
sonal
relations
).
Statistical Approach
Because
66
projects
did
not
report
any
knowledge
usefrom
other
divisions,
the
dependent
variable
“amount
acquiredknowledge”
was
set
to
zero
for
these
projec
ts.
Becauseof
this
largenumber
of
observations
with
a
valueof
zero,
a
least
squar
es
regression
model
was
inappropriate,and
I
employed
a
tobit
model,
using
maxi
mum
likelihoodestimation
(Maddala
1983,
Greene
1993).
In
addition,
the
statistical
analysis
of
completion
timewas
complicated
by
the
f
act
that
22
of
the
120
projectswere
still
ongoing
at
the
time
of
data
collection.
The
dataset,therefore,
includes
right-censored
cases
(Tuma
andHannan
1984).
Furt
hermore,
four
projects
were
e
the
dataset
contains
right-censored
data,
ordinaryleast
squares
regression
analysis
cannot
be
employed(Tuma
and
Han
nan
1984),
but
the
problem
of
right
censoringcan
be
dealtwith
by
using
a
hazard
rate
model.
Inthis
approach,
a
project
enters
the
risk
set
from
the
timeit
was
star
ted
and
leaves
the
risk
set
when
it
is
completedor
canceled.
The
instantaneoustra
nsition
rate
—
the
< br>dependentvariable
—
is
a
measure
of
the
likelihood
of
aproject
eit
her
completing
or
terminating
at
time
t
,
conditionalon
it
not
having
completed
or
terminated
before
t
.
The
higher
the
transition
rate,
the
more
likely
the
projectwill
be
completed
faster.
The
hzard
rate
model
takesthe
following
form:where
r
(
t
)
j
i
s
the
completion
rate
of
project
j
,
t
is
projecttime
in
the
risk
set,
and
r
(
t
)
j
*
is
t
he
completion
rate
includingthe
effects
of
all
of
the
control
variables
in
themodel.
The
effects
of
the
independent
variables
are
specifiedin
the
exponential
bracket
a
is
a
vector
of
estimatedcoefficients,
and
C
is
a
vector
of
independent
variable
s.
I
used
the
piecewise
exponential
specification
as
implementedin
the
statistical
program
TDA,
because
I
didnot
want
to
make
any
assumption
about
duration
de
pendencethat
would
require
a
specific
parametric
distribution.I
controlled
for
durat
ion
dependence,
however,
becausethe
survivor
plot
revealed
a
nonmonotonic
curv
e(cf.
Tuma
and
Hannan
1984).
The
plot
revealed
severaltransition
phases
occurrin
g
at
10,
12,
15,
18,
and
21months
and
I
therefore
enteredsix
time-period
variabl
esthat
reflect
the
time
distribution
of
events.
The
transitionrate
is
assumed
to
be
constant
within
these
periods,
andcovariates
are
assumed
not
to
vary
across
time
periods(Blossfeld
and
Rohwer
1995).
Because
multiple
projects
belong
to
a
division,
it
ispossible
that
project-
specifi
c
observations
are
nonindependentbecause
they
vary
with
divisional
attributes.I
th
erefore
chose
a
fixed
effect
specification
and
entered26
dummy
variables,
one
for
each
division
(except
one)that
ha
a
project
in
the
sample
(Greene
1993).
Theseta
ke
on
a
value
of
one
for
projects
belonging
to
the
division,and
zero
otherwise.
Because
the
variables
for
thealternative
explanations
do
not
vary
with
divisional
attributes,I
could
not
use
this
fixed
effect
specification
andomitted
thedummy
vari
ables
for
those
models.
Results
Descriptive
statistics
are
reported
in
Table
1,
and
resultspertaining
to
the
amou
nt
of
acquiredknowledge
and
projectcompletion
rate
are
presented
in
Tables
2
an
d
3,
1
and
2
in
Tables
2
and
3
present
theresults
for
the
alt
ernative
explanations
that
general
closenesscentrality
(i.e.,
path
length)
or
knowled
ge
relatedness(but
not
both
combined)
explainsthe
extent
of
knowledgeobtained
a
nd
product
development
time.
None
of
thesevariables
are
significant
in
these
mo
dels.
Project
teams
indivisions
with
short
path
lengths
in
the
entire
network
didn
ot
acquire
more
knowledge
(i.e.,
software
and
hardware)from
other
divisions
and
were
not
completed
faster.
Inaddition,
project
teams
for
which
many
other
divis
ionshad
related
knowledge
available
did
not
acquire
moresoftware
and
hardware
f
rom
other
divisions
and
were
notcompleted
faster.
These
results
show
that
neithe
r
the
extentof
related
knowledge
thais
available
in
the
Companynor
a
beneficial
network
position
consisting
of
short
pathlengths
in
the
entire
network
is
a
suffici
ent
factor
explainingthe
amount
of
interunit
knowledge
sharing
and
productdevelo
pment
independent
variables
predicting
the
extent
ofknowledgeacquired
f
rom
other
divisions
are
entered
inModels
3
and
4
in
Table
2.
The
main
effect
f
orthe
“closerelated”
variable
is
positive
and
significant.
divisions
with
a
high
deg
ree
of
closeness
centrality
(i.e.,short
path
lengths)
in
their
respective
knowledge
n
etwork
were
able
to
acquire
more
knowledge
from
other
divisions.
This
result
supports
Hypothesis
1.
The
results
for
the
independent
variables
predictingproject
completion
time
are
included
inModels
4
and
5in
Table
3.
The
main
effect
o
f
the
“close
-
related”
vari
ableis
positive
and
significant.
That
is,
projects
whose
divisionshave
a
high
degre
e
of
closeness
centrality
(i.e.,
shortpath
lengths)
in
their
respective
knowledge
net
work
werelikely
to
be
completed
more
quickly
than
those
with
alow
degree
of
c
loseness
centrality
(a
positive
hazard
rateabove
one
indicates
faster
completion).
This
result
supports
Hypothesis
2.
The
interaction
effect
for
the
outdegree
(i.e.,
direct
relations)and
transfer
diffic
ulty
variable(i.e.,
noncodifiedknowledge)
is
entered
in
Model
5
in
Table
3.
When
thisinteraction
effect
isadded
to
the
model,
the
main
effectfor
outdegree
to
relate
d
divisions
becomes
significant
andnegative,
while
the
coefficient
for
the
interacti
on
variableincluding
outdegree
and
noncodified
knowledge
is
positive.
Thus,
having
direct
relations
toother
divisions
in
the
project’s
knowledge
netw
ork
mitigatedthe
difficulties
in
transferring
noncodified
knowledge,but
the
net
effe
ct
of
having
these
direct
relationsled
to
longer
project
completion
time,
likely
be
cause
ofthe
maintenance
costs
involved
in
keeping
them.
Theseresults
lend
partial
support
to
Hypothesis
3a
and
full
supportto
Hypothesis
3b.
In
addition,
the
results
in
Model
5
in
Table
3
reveal
afew
other
interesting
fin
dings.
First,there
is
a
significantnegative
effect
for
the
indegree-
related
variable.
That
is,the
higher
the
number
of
related
divisions
that
come
tothe
focal
division
for
advice,
the
slower
the
completiontime
of
the
focal
project.
My
interpretation
for
this
effectis
that
focal
team
members
spend
time
helping
others
whocome
to
the
focal
division
for
advice,
leading
to
prolongedcompletion
time
of
the
focal
pr
oject.
Second,
projectteams
that
obtained
high
levels
of
knowledge
fromother
divi
sions
(i.e.,
the
first
dependent
variable)
completedtheir
projects
faster
than
those
t
hat
did
not.
Thus,controlling
for
network
relations,
the
use
of
existingknowledge
from
otherdivisions
led
to
higher
degrees
ofeffectiveness
as
measured
by
complet
ion
time.
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