It Takes Two To Tango
dual structural and temporal dynamics in inter

organizational
network evolution within the Dutch life sciences industry
Jan Faber
a
, Tom Poot
a
and Marius Meeus
b
a
Department of Innovation and Environmental Sciences, Utrecht Universit
y
b
Department of Organization Studies, Tilburg University
Paper prepared for presentation at the FIRB

RISC conference on
“Research and entrepreneurship in the knowledge

based economy”
September 7

8, 2009
KITeS

CESPRI, Bocconi University
Milan, Ita
ly
Jan Faber, Department of Innovation and Environmental Sciences, Utrecht University, P.O. Box 80125,
3508 TC Utrecht, The Netherlands. Phone: (+31)302531625. Fax: (+31)302532746. Email: J
.Faber@geo.uu.nl
Abstract
In order to avoid the problems of interpretation and biased estimates of the effects of dyad
specific combinations of two organizations’ individual characteristics on the existence of
their collaborative relation present in previous studies
of inter

organizational
collaboration, this study introduces a variance decomposition model of complex discrete
network dynamics. The model identifies the relative magnitudes of structural and temporal
dyad specific inducements as well as organization spec
ific opportunities to collaborate.
Furthermore, the evolution of collaborative mechanisms can be assessed by analyzing
successive waves of networks. The model has been applied in the analysis of the rather
volatile collaboration network of Dutch dedicated
life sciences firms (DDLSFs). The
results partly correspond with those obtained by Powell et al (2005) but in a more
articulated way. In the first period analyzed (2002

2004), structural preferential
attachment of DDLSFs, irrespective of their centrality,
to central partner organizations (i.e.
Dutch universities) and temporal mutual preferential attachment between the most central
DDLSFs and partner organizations account for 97% of the variance of collaborative
relations. In the second period analyzed (2004

2005), structural preferential attachment
and temporal mutual preferential attachment weakened while structural dyad specific
collaboration became dominant together accounting for 99% of the variance of
collaborative relations. Due to network membership a
nd the exploration of collaborative
relations via temporal mutual preferential attachment both DDLSFs and partner
organizations seemingly learned from which organization to obtain what they need. These
successive collaboration mechanisms also altered the s
tructure of the collaboration
network of DDLSFs with a stronger articulation of more central DDLSFs and a
diminishing articulation of central partner organizations because organizations relying on
structural dyad specific collaboration partly retreated fro
m the network. Consequently, the
opportunities of the remaining DDLSFs to establish beneficial collaborations deteriorated
and, accordingly, also the effectiveness of Dutch governmental policy stimulating the
development of a Dutch life sciences industry
1
I
ntroduction
Inter

organizational collaboration
has been
argued
to occur
due to
various
incentives
.
Incentives for such collaboration encompass
, inter
alia,
control over supply relations (Pfeffer
& Salancik, 1978), access to complementary resources (Hagedo
orn, 1993) and learning new
competences (Powell et al, 1996; Baum et al, 2000). Apart from these
organizational
i
ncentives to collaborate (i.e. inducements;
Ahuja, 2000

a
), actual inter

organizational
collaboration also depends on the prior position
s
of or
ganizations in the collaboration
network and
their
previous experiences with collaboration (Gu
lati & Gargiu
lo, 1999) (i.e.
opportunities;
Ahuja, 2000

a
). Both sources of effects on the collaborative behavior
of
organizations have been studied for various t
ypes of organizations but mostly from
a single
actor
perspective.
Much empirical research adopted this single node perspective in studying
dyadic collaborations, mostly for pragmatic reasons
(e.g. Powell et al, 1996; Eisenhardt &
Schoonhoven, 1996; Ahuja,
2000

a,b
; Sakakibara, 20
02
).
However none of these rearchers
would deny that a
collaborative relationship
results from
both
partnering organizations’
incentives and opportunities to collaborate
as ‘it takes two to tango’. Consequently,
features
of
both
no
des affecting
the occurrence of such a relationship should be investigated
simultaneously
because
the relationship itself
is
the subject
of interest.
So far,
there is
a
small number of
recent
studies
that actually did
incorporate
this perspective in their
research
of collaborative relations
among organizations
by taking dyads of organizations as
the unit
of analysis (
e.g.
Stuart, 1998;
Gula
ti & Gargiulo, 1999; Powell et al, 2005).
Unfortunately,
the
se
studies
lumped together
inducement and opportunity indic
ators of both partner
s
involved in a
dyad
in a
compound
form (difference, sum, ratio or product)
thereby obscuring
whether or not
the occurrence of
their collaborative relationship stems from the inducements
and opportunities of
each
or both of them
; for e
xample, ‘technical overlap of firms
i
and
j
’
and ‘sum of sales of firms
i
and
j
’ i
n Stuart (1998) and ‘joint centr
ality’ and ‘size’ in Gulati
& Gargiulo (1999).
Estimating effects of compound measures has methodological implications such as
biased estimate
s if the effects of the constituents of a compound measure are not estimated
as well (e.g. Fuguitt & Lieberson, 1974). Only if the effect of a compound indicator is
specified together with the effects of its constituent individual components then the
compo
und indicator represents a distinct concept. A nice case in point is the
analysis
conducted
by Powell et al (2005) that uses asymmetric features of partners and focal firms
2
such as their incentives to forge col
laborative relations. In their analysis they c
ontrolled
for
the opportunities to collaborate for every partner organization and the inducements to
collaborate of each focal firm in their analysis. This control was confined to the perspective
of the focal firms of interest trying to establish collabora
tive relations with partner
organizations. Controls for the opportunities to collaborate of each focal firm and the
inducements to collaborate of every partner organization are absent. This suggests as if only
a subset of incentives (inducements and opport
unities) of the focal firms and the partner
organizations are relevant in establishing collaborative relations between the focal firms and
their partner organizations. And in addition to these specification and estimation issues there
is the issue that the
effects of opportunities
to collaborate
on the establishment of
collaborative relations between organizations have been argued to reflect multiple
explanations simultaneously; these effects are regarded as “macro phenomena emerging out
of micro decisions
of organizations” (Gulati & Gargiulo, 1999: 1475). The question that
arises
from this discussion
is how repeatedly observed network relations should be analyzed
in order to avoid these problems of contamination of effects, biased estimates and interpreted
causalities.
The first step is to decompose the variation of dyadic network relations into mutually
exclusive sources of variation. Although such a variance decomposition is a descriptive
statistical analysis, it provides a complete set of distinctive inte
rmediate variables to be
explained by specific types of attributes derived from focused additional theory formation.
This is exactly what statistical social network analysis models do (e.g. Kenny & La Voie,
1984; Snijders & Kenny, 1999). The sources of var
iation specified in these models reflect
the multi

level character of networks, i.e. network, actors and dyad specific sources of
variation. Consequently, it
becomes
obvious how the effects of additional explanatory
network, actor(s) and linkage specific a
ttributes should be specified and also the effects of,
for example, compound dyad specific attributes constituted of actor specific attributes (e.g.
Duijn et al, 2004). These social network models are, however, specified for the analysis of
directed dichot
omous network
s
measur
ed at one moment in time. But
these models suffer
from three inadequacies regarding the analysis of repeatedly observed dichotomous non

directed networks like the inter

organizational networks analyzed by Gulati & Gargiulo
(1999) and P
owell et al (2005). First, the social network analysis models developed are
unable to uniquely identify the constituent variance components of non

directed networks.
Secondly, they lack specifications of autocorrelation in panel data on repeatedly observed
3
networks. Thirdly, they do not accommodate accepted insights into the role of the topology
of networks in establishing network relations (e.g. Barabási & Albert, 1999).
In order to circumvent these inadequacies in the analysis of repeatedly discretely
mea
sured non

directed networks, Faber & Poot (2009) developed a multi

level variance
decomposition model of structural and temporal dynamics in discrete non

directed networks.
This model does not only specify the multi

level variance components mentioned befo
re but
also their structural and temporal effects on the occurrence of dyadic network relations.
Additionally, the observed variance components are measured as topological properties of
networks (node degrees and density) so that the role of network topolo
gy in establishing
network relations is explicitly specified. Application of this model to two waves of data on
collaborative relations between Dutch dedicated life sciences firms (DDLSFs) and their
partner organizations not only provides statistical evide
nce of the existence of structural and
temporal collaborative relations between them and the effects of both partners’ inducements
and opportunities thereon but also of the succession of different collaboration mechanisms
over time.
This article is structu
red as follows. First, the model, the analyses conducted and results
obtained will be presented. In Section 2, the variance decomposition model representing
basic graph

theoretical concepts of discrete non

directed networks will be described together
with
its estimation from dichotomous data on the (non)existence of collaborative relations
among organizations. In Section 3, the data collection used, characteristics of the datasets
used and the results obtained from their statistical analysis will be present
ed. After that, the
implications of the results will be elaborated, discussed and related to previous studies. The
theoretical and policy implications of these results and their context will be explored in
Section 4. Section 5 ends this article with a disc
ussion of the research carried out and the
conclusions to be drawn.
1.
Variance decomposition of discrete network dynamics
1.1
Specification of the variance decomposition model
In this section,
first
the ideas and model of scale

free networks of Barabási & Alb
ert
(1999)
will be explained
.
A
nalysis of the assumptions behind their model reveals that there
4
is an important option to develop the
ir
ideas further. Next
,
it
will
be described
how our
variance decomposition model can deal with this challenge.
The startin
g point
for the development
of the variance decomposition model
was
the
study of Barabási & Albert (1999). Their assessment of hierarchically structured empirical
networks
being
reflected in
very right

skewed node degree distributions, i.e. power

law
distr
ibutions with
P(k)
k
and
k
representing the node degree, induced them to simulate
and investigate the preferential attachment
of new entrants to already
connected
nodes over
time
.
The results demonstrated that if
3
then the node degree distributions
become
independent of the size of the network;
which means that
a scale

free hierarchically
structured network appears.
Mitzenmacher (2004) proved mathematically that
right

skewed
node degree distributions result from
an underlying
geometric model whose p
arameters
determine the nature of the power

law distribution
as
, for example, exponential, log

normal
or Poisson.
Furthermore
, Clauset et al (2007) demonstrated that it is very hard to determine
from empirical data on node degree distributions which theore
tical distribution applies due
to noise and measurement errors in the data. They found
that
in many cases the empirical
distribution fitted equally well to
all
three theoretical distributions mentioned
above
,
especially for
<3
.
This implies
that the unde
rlying geometric model can be represented as a
linear model after log

transformation of the data because of their
close
correspondence with
the
log

normal distribution.
The underlying geometric model producing the results presented by Barabási & Albert
(19
99) can be specified as
N
ij
t
=
*
(N
i
∙
t
)
1
*
(N
∙
j
t
)
2
*
(N
∙∙
t
)
3
*
ij
t
(1)
with
=
1
,
1=
3=0
,
2>0
and
ij
t
=
1
.
In this model
N
ij
t
represents the (non)existence of a
collaborative relationship between organizations
i
and
j
at the moment
t
,
N
i
∙
t
denotes the
node (out)degree of organ
ization
i
at
t
,
N
∙
j
t
denotes the node (in)degree of organization
j
at
t
,
N
∙∙
t
denotes the total number of linkages in the network at
t
,
ij
t
represents the prediction
5
error in
N
ij
t
and
is
a constant.
N
i
∙
t
and
N
∙
j
t
reflect the
centrality
of organizations
i
and
j
,
respectively,
in
the network at
t
.
N
∙∙
t
reflects the density of the network at
t
. And
ij
t
reflects the effects of unidentified
dyad (i.e.
linkage or pair of actors
)
specific characteristics
at
t
.
Each effect
specified in eq(1)
is supposed
to oper
ate within the time period separating
two successive moments o
f observation (e.g. Fisher, 1970
);
a non

trivial issue that
wil
l be
readdressed
later
.
The geometric model
in eq(1)
specifies that the occurrence of a
collaborative relationship not only depends
on preferential attachment of organization
i
to
j
but also on that of organization
j
to
i
while both effects
are
controlled for the positive effect
of network density on that occurrence. Consequently, the Barabási & Albert (1999) model of
preferential att
achment in network dynamics is a partial
model of network dynamics
, which
can be developed further by allowing the other topological properties of the network
specified in eq(1) to exert their influences
.
Log

transformation of eq(1) results in
ln(
N
ij
t
) =
ln(
) +
1 ln(
N
i
∙
t
) +
2 ln(
N
∙
j
t
) +
3 ln(
N
∙∙
t
) + ln(
ij
t
)
or
L
N
ij
t
=
a
+
b1
L
N
i
∙
t
+ b2 LN
∙
j
t
+ b3 LN
∙∙
t
+ e
ij
t
(2)
where
a=ln(
)
,
b1=
1
,
b2=
2
,
b3=
3
,
e
ij
t
=ln(
ij
t
)
and
e
ij
t
~
N(0,
2
eijt
)
.
This linear model of
network dynamics specifies the
variance of the
(logarithm of the)
bilateral collaborative
relationship between any pair of organizations
i
and
j
at
the
moment
s
t
to consist of four
parts induced by
the distinguished
sources of variation
mentioned before
; in fact a two

way
analysis of v
ariance perspective of a graph is adopted
here
.
1
E
q(2) does
, however,
not differentiate
between time

independent or structural and time

dependent or temporal effects
yet
. Such effects cannot be derived directly from the
observations of
L
N
ij
t
,
L
N
i
∙
t
,
L
N
∙
j
t
and
L
N
∙∙
t
.
In order to differentiate between structural and
1
For the sake of the argumentation that follows the measurement of
N
ij
t
,
N
i
∙
t
,
N
∙
j
t
and
N
∙∙
t
on discrete scales
is left aside here but will be returned to in Section 2.2.
6
temporal effects of the sources of variation specified on the occurrence of a bilateral
collaborative relationship between any pair of organizations
i
and
j
at
t
,
the variance of
L
N
ij
t
i
s specified to consist of two parts, namely a structural and a temporal part. This is done by
representing the value of
L
N
ij
t
as the sum of the value of an unobse
rved structural latent
variable
M
i
j
∙
and the value of an unobserved temporal latent variable
M
ij
t
(e.g. Wheaton
et
al
, 1977)
. In the same way, the values of
L
N
i
∙
t
,
LN
∙
j
t
and
LN
∙∙
t
are represented as the sum of
an unobserved structura
l and
an unobserved
temporal latent variable.
In order to estimate the variances and covariances of the unobserved st
ructural and
temporal
latent
variables
M
at least two successive moments
t
and
t

1
of the observed
variables
L
N
must be specified, namely
L
N
ij
t
=
M
i
j
∙
+
M
ij
t
,
L
N
ij
t

1
=
M
i
j
∙
+
M
ij
t

1
,
L
N
i
∙
t
=
M
i
∙
∙
+
M
i
∙
t
,
L
N
i
∙
t

1
=
M
i
∙
∙
+
M
i
∙
t

1
,
L
N
∙
jt
=
M
∙
j
∙
+
M
∙
j
t
,
L
N
∙
jt

1
=
M
∙
j
∙
+
M
∙
j
t

1
,
L
N
∙∙
t
=
M
∙∙
∙
+
M
∙∙
t
and
L
N
∙∙
t

1
=
M
∙∙
∙
+
M
∙∙
t

1
. These
equations
constitu
te the measurement
model of the unobserved
structural and temporal latent variables
M
on the observed variables
L
N
. The variance of each structural latent variabl
e represents the common variance of two
successively observed
and log

transformed
variables
(i.e. their auto

covariance)
whereas the
variance of each temporal latent variable represents the unique variance of
a
log

transformed
observed
variable. Additional
ly, in accordance with eq(2), the following influence relations
between
the latent variables are specified,
M
i
j
∙
=
a
+
b1
M
i
∙
∙
+
b2
M
∙
j
∙
+
b3
M
∙∙
∙
+
e
ij
(3

a)
M
ij
t
=
b4
M
i
∙
t
+
b5
M
∙
j
t
+
b6
M
∙∙
t
+
e
ij
t
(3

b)
M
ij
t

1
=
b7
M
i
∙
t

1
+
b8
M
∙
j
t

1
+
b9
M
∙∙
t

1
+
e
ij
t

1
(3

c)
with
e
ij
~
N(0,
2
eij
)
,
e
ij
t
~
N(0,
2
eijt
)
and
e
ij
t

1
~
N(0,
2
1
eijt
)
.
7
Eqs
(3

a,b,c) constitute the variance decomposition model of
dual
structural and temporal
network dynamics. Both the measurement and the influence relations model can be specified
straightforward in the computer program
LISREL
®
(Jöresko
g & Sörbom, 1993) in order to
estimate the variances and covariances of the specified latent variables
M
and the
values of
the
unknown constant parameters
a
,
b
1

b
9
,
2
eij
,
2
eijt
and
2
1
eijt
.
The latter th
ree
parameters
are called the unique between

linkages and within

linkage variances of
L
N
ij
t
and
L
N
ij
t

1
.
The
unique between

linkages variance repr
esents the effects of unidentified
structural
dyad
specific characteristics. The unique within

linkage varianc
es are conceived as random errors
at
t
and
t

1
.
But in LISREL the residuals
e
ij
,
e
ij
t
and
e
ij
t

1
can also be specified as latent
variables acting as dependent variables in additional analyses.
In order to comply with the
specification of
an
econometric pan
el data model (e.g. Johnston & DiNardo, 1997)
constraints are imposed, namely
b4=b7
,
b5=b8
and
b6=b9
, so that the effects of the
temporal latent variables apply to every moment
t
and
t

1
in
the observed
time period
T
.
The combined model specified in LISREL
can be estimated by means of, with increasing
accuracy, the Unweighted Least Squares (ULS), the Generalized Least Squares (GLS) or the
Maximum Likelihood (ML) method. All three methods estimate the unknowns in the
LISREL model from
an
input matrix
S
conta
ining the variances and covariances of the
observed variables
L
N
and their additionally provided
mean values
. Saris & Stronkhorst
(1984) recommend
to use the ULS method only when
S
is nearly not positive definite.
2
After
completion of the estimation LISREL
produces various measures of fit, of which the
Goodness of Fit Index (GFI), the Adjusted Goodness of Fit Index (AGFI) and the Root
Mean Square
Residual (RMSR
) will be presented
together
with the
model
estimates
.
2.2
Correlations from discrete data
The l
inear variance decomposition model of network dynamics specified in LISREL
assumes that all observed network indicators
N
ijt
,
N
ijt

1
,
N
i
∙
t
,
N
i
∙
t

1
,
N
∙
jt
,
N
∙
jt

1
,
N
∙∙
t
and
N
∙∙
t

1
are measured on
an interval or ratio scale
. But with discrete measurements of
network
indicators
they are not;
N
ijt
and
N
ijt

1
are even measured as dichotomous variables having a
2
Nevertheless, if
S
is nearly positive definite then boundary problems of convergence may occur resulting in
insignificant negative estimates of one (or more)
variance(s). Such problems can be solved by fixing the value
of each such a variance equal to zero.
8
value of either one or zero. To solve this discrepancy, the polychoric correlation coefficient
described
by Olsson (1979) and programmed in PRELIS
(Jöres
kog & Sö
rbom, 1995)
has
been applied.
The polychoric correlation coefficient represents the association of two normally
distributed
standardized continuous
latent variables, which lay under two observed discrete
variables;
in other words,
every observed di
screte variable is conceived as the discrete
realization of an underl
ying continuous
latent
variable. This approach
also
has been
successfully
applied in the analysis of
the
succession
of
individual discrete
directed
network
relations
(e.g. Schweinb
erger &
Snijders, 2007). Before estimation of the polychoric
correlation coefficient
, the binomial or multinomial distribution of each observed discrete
variable is
‘
folded
’
under the normal distribution of a standardized
continuous
latent variable
.
Whether or no
t the binomial or multinomial distribution is skewed
and/
or peaked is irrelevant;
in case of skewed distributions the
‘
folding
’
represents a nonlinear data transformation like
the
log

transformation.
Subsequently
,
the minimum variance unbiased estimate of
the
correlation of every pair of standardized
continuous
latent variables is
derived
by means of the
ML method
. In a Monte Carlo study of various measures of
the
association
for
observed
discrete variables this polychoric correlation
is
demonstrated to out
perform all other te
sted
measures of association (Jöreskog & Sö
rbom, 1995:
10

17
).
The estimated polychoric correlations between the observed
discrete
network indicators
N
ijt
,
N
ijt

1
,
N
i
∙
t
,
N
i
∙
t

1
,
N
∙
jt
,
N
∙
jt

1
,
N
∙∙
t
and
N
∙∙
t

1
are the
standardized covaria
nces
of the continuous latent variables underlying them, which are placed
in the input matrix
S
to
be analyzed by LISREL. The means and variances of
the standardized continuous latent
variables
are by
definition
equal to zero and one, respectively.
3.
Da
ta,
characteristics
and results
3.1
Data collection
The data used for testing the variance decomposition model and assessing the various
dynamics in an empirical discrete network have been obtained from the Dutch BioPartner
Program. This program was started
in 2000 by the Ministry of Economic Affairs to stimulate
entrepreneurship in the life sciences as well as the entire industry (Ministry of Economic
Affairs, 1999). The program provided
advisory services and
seed capital for life sciences
9
based new product
development by Dutch ventures and start

ups. Furthermore, a registration
of Dutch firms active in the life sciences industry was set up in order to monitor the
development of these firms by means of a yearly survey since 2002. Participation in these
survey
s was obligatory for firms that received funding from the BioPartner Program
. The
program ended in 2005.
As of 2002 till 2005
four
annual
surveys were sent out. In these
surveys, except
for the
2003
survey
, the Dutch dedicated life sciences firms (DDLSFs)
were
asked
to list
names of their five most important partner organizations. The maximum of 5
partners to be mentioned induces, however, a truncation of the total number of established
relations, which was also asked for in each survey.
This truncation
re
sults in
data on
the
actual collaborations of DDLSFs with individual partner organizations
that
represent
about
80% of all collaborative relations of DDLSFs
in 2002, 2004 and 2005. Due to the omission
of data on partner specific collaboration of each DDLSF
for 2003, the data for 2002, 2004
and 2005 represent two periods of unequal length. Therefore, the data have been analyzed as
two successive waves (2002

2004 and 2004

2005) and not as successive moments of
observation in one
set of panel data
.
Each wave h
as been analyzed separately via estimation
of the variance decomposition model specified.
The data on actual collaborations of DDLSFs with partner organizations differ in nature
from the data on announced collaborative agreements
between organizations
used
by
, among
others, Gulati & Gargiu
lo (1999) and Powell et al (2005). The data on actual collaborations
may reflect besides formal
ized
also informal activities. Furthermore, these data show clearly
whether or not actual collaboration is continued,
terminate
d
or disrupted after one year.
But
t
he dynamics of actual collaboration taking place within one year remain unobserved. In this
respect, the data on collaborative agreements give more insight into
the speed of
their
conclusions and by that
into
the intensi
ty of collaboration
. However,
these data provide no
insight into when collaborative agreements come to an end
(e.g. Ahuja, 2000

a,b)
.
Hence
, it
remains unknown whether successive collaborative agreements between two organizations
accumulate and strengthen
the
ir
collaboration or that they
succeed one another and
only
continue the collaboration or that they reflect the reestablishment of their collaboration
after
disruption.
And
what
if those organizations do not conclude successive collaborative
agreements o
r less than before
?
I
s that indicative of ended or less collaboration between
them or of successful continuation of a p
reviously concluded agreement? So, b
oth types of
data
on collaborative relations between organizations have their peculiarities. The adva
ntage
of the data on actual collaborations is that they
provide more information about
the ecology
10
of such relations than the data on announced collaborative agreements. But the latter type of
data
provide more information about
the speed and
changes in in
ten
sities of collaborations
than the data on actual collaborations.
3.2
Characteristics of the data
From the 2002 and 2004 surveys 108 DDLSFs were derived that at least in one of them
replied to the question on partner names. These 108 DDLSFs said to co
operate with 220
partner organizations (of which 11 and 8 DDLSFs in 2002 and 2004, respectively).
Additional desk research was done to find out when a DDLSF responded only to either the
2002 survey or the 2004 survey whether or not it already existed in 20
02 or ceased to exist in
2004. DDLSFs not existing in 2002 or 2004 were assigned missing values for that particular
year and left out in further analyses of the observed variables via pairwise deletion. From the
2004 and 2005 surveys 96 DDLSFs were derived
that at least in one of them replied to the
question on partner names. These 96 DDLSFs said to cooperate with 179 partner
organizations (of which 8 DDLSFs in both years). As in the previous wave, DDLSFs not
existing in 2004 or 2005 were assigned missing v
alues for that particular year and left out in
further analyses of the observed variables via pairwise deletion.
The status of DDLSFs, partner organizations and their collaborative relations and
changes thereof are presented in Table 1 together with the bi
rth and death of organizations
and their relations
. The column totals represent the situation of the DDLSFs, partner
organizations and their collaborative relations at the beginning of each observation period
whereas the row totals represent their situatio
n at the end of each observation period. The
reported percentages in Table 1 indicate the part of the total number of collaborative
relations reported by the DDLSFs in the surveys that are included in both data sets
From the figures on the organizational a
nd relational ecologies contained in
both
data
set
s
presented in Table 1
it can be derived that the population of
collaborating
DDLSFs
remains rather
stable
; 71% of the DDLSFs in 2004 already existed in 2002 and 88% of the
DDLSFs in 2005 already existed in
2004.
However,
the
ir
collaborative network is
very
volatile between
2002,
2004 and 2005;
75% of t
he relations in 2002, 69%
of the relations in
2004
and 58% of the relations in 2005
were changed within one year. Furthermore, the
density o
f this network is
extremely low
,
i.e.
0.8
%,
1.0
% and
0.8
%
in
200
2, 2004 and 2005,
respectively.
11
Table 1. Organizational ecologies of DDLSFs and partner organizations and the ecology of their collaborative relations
total
2002
2004
conn. disconn. org. death
total
2004
2005
conn. disconn. org. death
connected
DDLSFs disconnected
org. birth
64
18
33 26 5
18
26
77
26
45 32
16 10
3
DDLSFs total
2004 / 2005
77 26 5
64 32 10
connected
Partner Orgs.
org. birth
137
48 89
83
131
48 83
48
Partner Orgs total
2004 ./ 2005
131
89
96 83
establ. relations
Collab. new relations
from
:
Relations

disconn. orgs.

org. birth
189
(77%)
47 130 12
90
61
198
62 136
76
10
Collab. total
Relations 2004./.2005
198 130 12
(81%)
148 136
(90%)
12
Insight into the hierarchical structure of the network can be derived from the descriptive
statistics for the distributions of the node degrees > 0 of th
e DDLSFs and their partner
organizations in
2002,
2004 and 2005, respectively.
Table 2
. Weighted binomial moments
3
and power degree
of the node degree distributions
DDLSFs
partners
2002
2004
2005
2002
2004
2005
mean
2.95
2.57
2.31
1.38
1.51
1.54
varianc
e
2.
36
1.93
1.84
1.06
2.28
2.21
skewness
0.
30
0.4
5
0.
67
2.99
3.80
3.48
kurtosis

0.
85

0.99

0.80
8.46
16.25
12.47
power degree
0.13
0.55
0.86
2.08
1.84
1.77
N
64
77
64
137
131
96
As skewness and kurtosis are zero for a
n approximated
normal distri
bu
tion, it can be
derived from T
able 2
that the
centrality
of DDLSFs represented by their node degrees is
virtually normally distributed in
all ye
ars of observation
(e.g. Faber,1988)
.
The skewness
and kurtosis of the distributions of node degrees of the part
ner organizations in
2002,
2004
and 2005 show that there is a clear hierarchy in the
centrality
of partner organizations
during
the
years
of observation
with a few partner organizations having many collaborative
relations with DDLSFs and many partner organ
izations having only one or a few
collaborative relations with DDLSFs.
Although the power degree decreases over time it
remains
substantial
, which is
also indicative of the hierarchy in the
centrality
of partner
organizations.
From
these statistics it may
be derived that the network of collaborative
relations of DDLSFs is more likely to be structured by the partner organizations
than by the
DDLSFs themselves.
This preliminary conclusion is supported by the membership of organizations of the top

10 of node d
egrees of DDLSFs and partner organizations in the years 2002, 2004 and 2005.
Only one of the 10 most
central
DDLSFs in 2002 appears also in the top

10 of 2004.
The
same is true for the top

10 of DDLSFs in 2004 and 2005. Regarding the top

10 of most
3
As the moments of the multinomial distribution of a discrete variable with more than 2 categories are
undefined, they are approximated here by using the m
oments of the binomial distribution of every category and
weighting them by the number of categories identified (e.g. Evans et al, 2000).
13
central
partner organizations another
picture emerges:
six
of the partner organizations in the
top

10 of 2002 are also
in t
he top

10 of 2004. I
n the top

10 of 2004 and
that of
2005 seven
partner organizations are the same. These stable most
central
partner organi
zations are all
Dutch universities
with
life sciences departments.
The dynamics in the network of collaborative relations of DDLSFs
over time
will be
investigated in the next section based on estimation
s
of the variance decomposition model of
such dynamics
presented before.
T
he large volatility of collaborative relations after one
observation
period
reflects that the effects of inducements and opportunities of both parties
to collaborate
predominantly
operate
within
that period
.
Therefore,
these effects are
specified to occur at the same moment of observation as the best approximation of effects
operating within
any
period of observation
(Fisher,
1970
).
3.3
Results on
variance decomposition of
network dynamics
The estimations of the various unknown constan
t parameters in the variance
decomposition model specified in LISREL by means of one of the three methods mentioned
before
are based on the input correlation matrices
S
presented in the Appendix (N =
23760
and N =
17184
). The elements of
S
are polychoric c
orrelations estimated by means of
PRELIS. In the Appendix the correlations of the entire network indicators
N
∙∙
t
and
N
∙∙
t

1
are
not presented because with two years of observation
in each wave
there is only one
observation available for each indicator res
ulting in standardized variances and covariances
of these indicators equal to zero. So, the effects of
an
increasing or decreasing
density
of the
network on the establishment of a collaborative relation between a DDLSF and a partner
organization cannot be
estimated. But as the density of the network of collaborative relations
of DDLSFs hardly changes over the
four
years of observation, the effects of changes in
network density
may
be
conceived as
very small
. Furthermore, as standardized variables
have zero
means also the intercept
a
is by definition equal to zero and accordingly not
specified. These limitations affect the final specification of the variance decomposition
model in LISREL as follows. The measurement mod
el of unobserved structural and
14
temporal
latent variables contains the following equations
L
N
ij
t
=
M
i
j
∙
+
M
ij
t
,
L
N
ij
t

1
=
M
i
j
∙
+
M
ij
t

1
, L
N
i
∙
t
=
M
i
∙
∙
+
M
i
∙
t
,
L
N
i
∙
t

1
=
M
i
∙
∙
+
M
i
∙
t

1
, LN
∙
jt
=
M
∙
j
∙
+
M
∙
j
t
and
LN
∙
jt

1
=
M
∙
j
∙
+
M
∙
j
t

1
and the influence relations model in eq(3

a,b,c) reduces to
M
i
j
∙
=
b1 M
i
∙
∙
+
b2 M
∙
j
∙
+
e
ij
(4

a)
M
ij
t
=
b
3
M
i
∙
t
+
b
4
M
∙
j
t
+
e
ij
t
(4

b)
M
ij
t

1
=
b
3
M
i
∙
t

1
+
b
4
M
∙
j
t

1
+
e
ij
t

1
(4

c)
Each
LISREL model containing
the reduced variance decomposition model
has been
estimated from
one of the input correlation matrices
S
in the Appendix. This is done by
means of the ULS method becaus
e both correlation matrices contain
four correlations that
are virtually zero
thereby
making
S
in each
analysis
nearly not positive definite as
mentioned
before. The ULS estimates of the various unknown constant parameters
in
the
models
for
the
periods 200
2

2004 and 2004

2005
are presented below in table
s
2
and 3
, respectively
(all
with
p < 0.001
).
The fit of the reduced variance decomposition model to each input matrix
S
is excellent as reflected by the values of the GFI, AGFI and RMSR measures.
Table 2.
LISREL estimates of the variance decomposition model of discrete
collaborative network dynamics of DDLSFs, 2002

2004
b1
=

0.2394
var(M
ij
t
)
= 0.2661
var(M
i
∙
t
)
= 0.8403
b2
=
4.6613
var(M
ij
t

1
)
= 0.2661
var(M
i
∙
t

1
)
= 0.8321
b3
=
0.4333
var(M
i
j
∙
)
=
0.7339
var(M
i
∙
∙
)
= 0.1638
b4
=
0.3035
2
eijt
= 0.0178
var(M
∙
j
t
)
= 0.9830
2
1
eijt
= 0.0224
var(M
∙
j
t

1
)
= 0.9497
2
eij
= 0
*
var(M
∙
j
∙
)
= 0.0333
*
: fixed at zero after
an
insignificant negative est
imate
(p>0.10)
GFI = 0.999; AGFI = 0.998; RMSR
=
0.014
15
Table
3
.
LISREL estimates of the variance decomposition model of discrete
collaborative network dynamics of DDLSFs
, 2004

2005
b1
= 0.5623
var(M
ij
t
)
=
0.1977
var(M
i
∙
t
)
=
0.9994
b2
= 1.9066
var(M
ij
t

1
)
=
0.1977
var(M
i
∙
t

1
)
=
0.9616
b3
= 0.3351
var(M
i
j
∙
)
=
0.8023
var(M
i
∙
∙
)
=
0.0191
b4
= 0.3018
2
eijt
=
0.0025
var(M
∙
j
t
)
=
0.9104
2
1
eijt
=
0.0095
var(M
∙
j
t

1
)
=
0.8803
2
eij
=
0.4167
var(M
∙
j
∙
)
=
0.1044
GFI = 0.999; AGFI = 0.999; RMSR
=
0.014
The polychoric variance of dichotomous network relations
(= 1.0)
contains a structural
variance
(
var(M
i
j
∙
)
)
of 0.734 (73.4%) and 0.802 (80.2%) in 2002

2004 and 2004

2005,
respectively. For the independent
node degrees of DDLSFs and partner organizations another
picture appears. The structural variances of the node degrees of DDLSFs
(var(M
i
∙
∙
)
)
and
partner organizations
(var(M
∙
j
∙
)
)
are only 0.164 (16.4%) and 0.033 (3.3%) in 2002

2004 and
0.019 (1.9%) and
0.104 (10.4%) in 2004

2005
, respectively
. This implies that the node
degrees of DDLSFs and partner organizations are very volatile over time; with
an
increasing
volatility of the DDLSFs’ node degrees and
a
diminishing volatility of the partners
organizatio
ns’ node degrees.
The structural variance of dichotomous network relations
in 2002

2004
(var(M
i
j
∙
)
=
0.7339
)
consists of a variance
equal to
0.009 induced by the structural variance of the node
degree of DDLSFs
(
b1
2
*
var(M
i
∙
∙
)
)
,
a variance
equal to
0.717 induced by the structural
16
variance of the node degree of partner organizations
(
b2
2
*
var(M
∙
j
∙
)
)
and a variance
equal to
0.0075 induced by the structural variance of unidentified
dyad
specific characteristics
(
var(M
i
j
∙
)

b1
2
*
var(M
i
∙
∙
)

b2
2
*
var(M
∙
j
∙
)
)
. So, 98% of the structural variance of
dichotomous network relations is explained by the structu
ral variance of the node degree of
partner organizations in 2002

2004. This result reflects a very strong preferential attachment
of DDLSFs in 2002
as well as
2004, irrespective of their
centrality
in the network
, to a few
very central
par
t
ner organization
s over time.
During the period 2004

2005, the
structural part of the
polychoric variance of
dichotomous network relations (= 1.0)
equals 0.8023 (80.2%) and
consists of a variance
equal to
0.006 induced by the structural variance of the node degree of DDLSF
s, a variance
equal to
0.380 induced by the structural variance of the node degree of partner organizations
and a variance
equal to
0.417 induced by the structural variance of unidentified
dyad
specific
characteristics like, for example, mutual complementa
rities of specific
technologies
. So, in
2004 and 2005 only 47% of the structural variance of dichotomous network relations is
explained by the structural variance of the node degree of partner organizations and 52% by
the structural variance of unidentifie
d
dyad
specific characteristics. After comparison of
these results with those for 2002

2004 it can be derived that the preferential att
achment of
DDLSFs to a few
very central
partner organizations has become weaker and that
dyad
specific partnering, irresp
ective of the
centrality
in the network
of the DDLSF and partner
organization involved, has become stronger in partner selection for collaboration and the
structuring of the collaboration network of DDLSFs. The weakened influence of structural
preferential
attachment is also reflected in the smaller estimate of
b2
being
1.907
for
2004

2005
against
4.661
for
2002

2004.
The temporal variances of dichotomous network relations in the years of observation
(
i.e.
var(M
ij
t
)
for 2004 and 2005 and
var(M
ij
t

1
)
for 2
002 and 2004, respectively) are 0.266
(26.6%) in 2002

2004 and 0.198 (19.8%) in 2004

2005; that is a reduction of
nearly
7% of
the total polychoric variance
o
f dichotomous network relations.
These temporal variance
s
are
almost entirely induced by
both
a
re
lative
stronger effect of the temporal variance of the
node degree of DDLSFs and a
relative
weaker effect of the temporal variance of the node
degree of partner organizations
(
b3
b4)
. So, the temporal variance of network relations
17
seems to be induced by
u
nequal
mutual preferential attachment between more
central
DDLSFs and partner organizations. But the resulting temporal collaborative relations
between these typical DDLSFs and part
ner organizations do not last
and the influence of
temporal unequal mutual
preferential attachment diminishes over time
.
4. Exploration of i
mplications
4.1 Introduction
The results of the variance decomposition model reveal that this model distinguishes
structural as well as temporal mechanisms of network dynamics whereas in pr
evious studies
of dyadic inter

organizational collaborative relations (e.g. Gulati & Gargiulo, 1999; Powell
et al, 2005) only structural mechanisms of network dynamics are assessed. Furthermore, the
results show that by analyzing successive waves of data o
n
an inter

organizational
collaboration network also the succession of structural and temporal structuring mechanisms
can be assessed. The theoretical implications of these more detailed results and insights will
be
explored
in Section 4.2
.
However, the
es
timated
variance decomposition model is a descriptive statistical model
that distinguishes methodologically distinct sources of variation, which themselves are only
components and no explanation of the variation in dyadic collaborative relations;
otherwise
the components would not explain 100% of the variance of dyadic collaborations. This
implies that
the effects of the variance components (network, actors, dyad and linkage)
reflect only empirical regularities instead of an explanation (e.g. Faber & Schepe
r, 2003).
Consequently, the variance components themselves need to be
additionally
explained by
network, actor, dyad and/or linkage specific attributes in order to provide an explanation of
dyad specific collaborations. So, the results obtained from the va
riance decomposition model
help to focus additionally required theory formation. The question then arises from the 100%
explanation of the variance of dyad specific collaboration whether or not the results and
network structuring mechanisms identified from
them make sense or are artifacts of the
variance decomposition model specified. This question will be addressed in Section 4.
3
by
relating these results to the then prevailing policy context and results
obtained
from
qualitative research into the strategi
c behaviors of
some
DDLSFs.
Subsequently,
the
implications of the results for the policies pursued by the Dutch government are
explored
.
18
4.
2
Theoretical implications
In this section the theoretical implications of the results presented before will be
com
pared with those derived by
Stuart (1998),
Gulati & Gargiulo (1999) and Powell
et al
(2005)
as they also applied a relational perspective in their empirical research
of inter

organizational collaborations
. In
all
these
studies positive structural effects o
f previous ties
on the establishment of collaborative relations were found. This effect is confirmed in this
study by the structural variances of collaborative relations
(var(M
i
j
∙
))
, which reflect
the
common variance
(i.e.
auto

covariance
)
of collaborative relati
ons over time.
Powell et al
(2005)
found that
this effect
operates
without an accompanying effect
of the collaborating
organizations’ network positions whereas
Stuart (19
98) and
Gulati & Gargiulo (1999)
found
that
this effect
operate
s
besides a
n independent
positive effect of their joint network position.
In this study, which disentangles the structural and temporal network position
s
of
individual
organizations participat
ing in bilateral collaborative relations, the structural positive
autocorrelation of successive collaborative relationships
, that is the positive effect of the
dyadic history of collaboration,
is found to depend strongly on the
structural
network
positions
of the partner organizations irrespective of the
structural
network positions of the
DDLSFs involved.
In other words,
the
large
structural
centrality
of the partner organization
s
stimulated
the
persistence
(or
unobserved repetition
within one
period of ob
servation
) of
bilateral collaborative relationship
s
. This result
reflects
the concept of ‘structure begets
behavior’ utilized in complex network analysis (e.g. Strogatz, 2001). As the structural
collaborations of the DDLSFs with especially Dutch universiti
es
containing
life sciences
departments are for 24% in 2002, 46% in 2004 and 40% in 2005 initiated by those
universities
(including spin

offs)
it can be derived that the preferential attachment of these
DDLSFs to the universities originated from both sides
; that is, universities searched for
and
established
DDLSFs and DDLSFs searched for universities to exchange
or combine
complementary resources. But as the DDLSFs’ structural network positions in terms of
centrality
play an insignificant role in this proce
ss in terms of explanatory power,
the
concept of
liability of dis
connectedness (Powell et al, 1996) receives little support from the
res
ults presented in this study.
The liability of disconnectedness does get some support with the results from a temporal
p
erspective.
Temporarily less
central
DDLSFs are less likely to collaborate with a partner
19
organization than temporarily more
central
DDLSFs. But
, simultaneously,
temporarily less
central
partner organizations are
also
less likely to collaborate with a DDLS
F than
temporarily more
central
partner organizations
.
These
results
can
also be interpreted as that
m
ore connected DDLSFs tend to collaborate temporarily with more connected partner
organizations
just
because both are more connected
, which allows them to
explore
mutual
collaborative relationship
s
without
severe penalties of a failure.
This
interpretation
coincides
with the finding of Powell et al (2005) that the most central organizations in the
ir
network
adopt
ed
a diversification mechanism in their collab
orative behavior next to a preferential
attachment mechanism.
The former mechanism is argued to be related to the exploration of
collaborative relationships
whereas
the latter mechanism is argued to be related to the
exploitation of collaborative relations
hips.
The mechanism of
diversification
is
also
argued
to incorporate novelties in the network and is therefore considered as
an important source
of
change within the network.
The mechanism
of preferential attachment
is also found in this
study
but
as struc
tural preferential attachment of DDL
SFs to Dutch universities.
T
he
results
on temporarily collaboration
also
do not
provide
much
support
for the concept of
liability of
dis
connectedness
hindering DDLSFs in establishing collaborative relations with partner
organizations
as
they are also searched for by partner organizations.
Another implication of the results presented in this study is that different collaboration
mechanisms operate simultaneously in the collaboration network of DDLSFs as found
before by Pow
ell et al (2005) for American
dedicated biotechnology firms
in the Boston area
but also that these mechanisms evolve over time. Structural preferential attachment and
temporal mutual preferential attachment both become weaker
whereas
structural dyad
specif
ic collaboration
becomes stronger
irrespective of
the participating organizations’
centrality
. This implies that
at
first
centrality
in the network
is crucial for an organization’s
access to information, knowledge or other complementary resources but that
later the
organization seems to have learned where to get them
from actual collaboration(s) an
d
information diffusion via indirect ties (Ahuja, 2000

b)
.
So
,
membership
of a network during
a longer period of time decreases the organization’s dependency on
i
ts
network position for
establishing collaborative relations with other organizations. Consequently, learning about
the network and its members
as a result of
continued membership of that network
plays
an
important role i
n the evolution of collaboration
me
chanism
s (de)structuring the network.
More i
nsight into the content and conditions facilitating this kind of
organizational
learning
requires
,
however,
further research.
20
From these insights the following image of the evolution of collaborative behaviors of
DDLSFs and their partner organizations appears. At the beginning when the collaboration
network emerged, DDLSFs established collaborative relations
, irrespective of their
centrality,
preferentially with Dutch universities occupying central positions in th
e network.
It seems as if
Dutch universities have selected DDLSFs to collaborate with quite at random
via trial and error. But the DDLSFs and universities having established multiple
collaborative relations
also
searched
for
each other
to explore more bene
ficial collaborative
relations as they were not
solely
dependent on each other
for immediate survival
. As a result
of the information gathering via this exploration and
the
established direct and indirect ties
,
several DDLSFs and universities learned quick
ly to establish collaborative relations
between
them
that were seemingly more beneficial in terms of contents exchanged
or combined
irrespective of their network positions. These
DDLSFs and universities decreased their
numbers of collaborative relations as
they (partly) found what they were looking for. With
their retreat from the tails of the node distributions of DDLSFs and partner organizations, the
node distribution of DDLSFs got more skewed to the right and that of the partner
organizations got less sk
ewed to the right as
is b
est
reflected
by
their, respectively
,
increasing
and decreasing power degrees (T
able 2)
. T
his
may
i
mply
that for the remaining
DDLSFs and partner organizations, especially new entrants, it may have become more
difficult to establis
h beneficial relations. For these DDLSFs the guidance of search based on
the centrality of partner organizations as an indicator
of opportunities to find a
beneficial
relation diminishes. At the same time, with a sharper articulation of the centrality of s
ome
remaining DDLSFs it becomes clearer for remaining partner organizations which DDLSFs
offer more opportunities for establishing a benefic
ial collaboration. Extrapolation
of these
tendencies over time indicates that
the collaborative behavior of DDLSFs a
nd partner
organizations based on (mutual) preferential attachment will have diminished further while
that based on structural
dyad
specific characteristics will have increased. As a result, it may
be expected that
eventually
the
growth
of the network will
slow down
due to
deteriorating
chances of survival for new and non

connected DDLSFs. This implies that the concept of
liability of disconnectedness (Powel et al, 1996)
was not
operational during the emergence
of the collaboration network of DDLSFs but
mor
e likely
later
when
the network
had
developed further
.
Nevertheless, it should be
recalled
that these structural aspects of the
evolution of collaborative behaviors of DDLSFs and their partner organization and the
resulting collaboration network are far fr
om deterministic. They exert their influences
on
a
21
rather bumpy
series
of very volatile
successive
collaborations with fast chang
ing
opportunities and uncertainties
for the
participating organizations.
4.
3
Policy context and implications
The two

way stru
ctural preferential attachment of DDLSFs to especially Dutch
universities
with
life sciences departments irrespective of the DDLSFs’ network position
s
may be related to two contextual situations, namely an already developed network of
collaborative relatio
nships of DDLSFs
and/
or specific Dutch governmental policies
implemented
. The first contextual situation of an already developed collaborative network
wherein the participants have become acquainted with each other is rather unlikely. 56%,
65% and 71% of t
he DDLSFs active in 2002, 2004 and 2005, respectively, were established
since 2000 when the BioPartner Program of the Ministry of Economic Affairs was
implemented. One might even say that after implementation
of the program the collaboration
network of DDL
SFs emerged as a result of the sharp increase of foundation
s
of DDLSFs
seemingly stimulated by the provision of seed capital subsidies
and
advisory services
. The
subsidies
might
even be
en
the reason for the low organization
al
death rate of DDLSFs
during th
e
observed period 2002

2005. So, the age of the co
llaboration
network of DDLSFs
seems not to be reason for the two

way structural preferential attachment within the network
an
d the absence of liability of dis
connectedness hindering DDLSFs to establish coll
aborative
relations.
The second contextual situation of specific Dutch governmental policies
implemented
seems more likely to have resulted in the two

way structural preferential attachment.
Frustration about good scientific IT research and virtually no IT
industry development
during the 80’s and 90’s of the last century, the virtual absence of an established
pharmaceutical industry that might trigger biotech
nology based
industry development and
good life sciences research reflected by the successful partic
ipation of several Dutch
academic
research groups in the Human Genome Project together induced the Dutch
government to take up an active role in life sciences ind
ustry development. T
his active role
consisted of the implementation of
the BioPartner Program
providing new ventures and
start

ups in the life sciences industry with seed capital and
advisory services
.
In the same
period
a public debate
developed
about the role of universities in society
and
the economy
and their participation in knowledge valoriza
tion.
After time
, all Dutch universities set up
22
incubators and technology transfer offices and adopted a policy of active knowledge
utilization. This resulted in a growing number of spin

offs, especially from the life sciences
departments, which also recei
ved funding from the BioPartner Program. Another result was
that the technology transfer offices began actively searching for DDLSFs that could benefit
from especially R&D collaboration with life sciences departments within the universities.
So, DDLSFs wer
e not alone in their search for beneficial collaboration as they were also
searched for by Dutch universities. In this context, DDLSF

university collaboration
gave
legitimacy to both their
activities
but also to the life sciences industry policy pursued by
the
Dutch government.
This policy has been successful in two
respects
, namely
a sharp increase of the number
of DDLSFs
founded and
the
survival of DDLSFs during thei
r first years after founding
.
The
DDLSFs operate
d
only
to a
limited
degree
outside their i
nitial technological field of
expertise (van der Valk et al, 2009) and change
d
after time from
either
a product
/service
or
technology
development strategy to a hybrid strategy of
technology development
on the
short term and product
/service
development on t
he long term (Willemstein et al, 2007)
in
order to increase their chances of survival
. The change after
time
to a hybrid strategy within
the initial technological field indicates that DDLSFs became more aware of how to increase
their (limited) chances of s
urvival and what they needed to improve them and where to get it.
Consequently, more content related dyad specific collaborations may be expected to have
developed as is reflected by the results of the variance decomposition model.
However,
the policy purs
ued by the Dutch government with the BioPartner Program
seems to have a paradoxical outcome on the long term for Dutch life sciences industry
development. The provision of seed capital and advisory services helped DDLSFs with their
founding and survival du
ring their first years of existence.
But as explained in the previous
section, after time
various
members of the collaboration network of DDLSFs found
seemingly more beneficial collaborations with established partner organizations, irrespective
of their ne
twork positions, that induced both of them to ret
reat partially from the network.
Thereby it
seems to have become
more difficult for the remaining DDLSFs and new entrants
to find and establish beneficial collaborations, which resulted
for them
in deteriora
ting
chances of survival. Consequently, it seems as if the initial success of the BioPartner
Program lost its effectiveness after time as a result of the dynamics of successive
collaboration mechanisms that it provoked.
23
An
other
intriguing issue to be addre
ssed is
the
large
volatility of the collaborative
relations of DDLSFs
.
Only 47 and 25 of the 189 collaborative relations of DDLSFs in 2002
survived in 2004 and 2005, respectively; that is 25% after two years and 13% after three
years. It seems as if
many
D
DLSFs
were
wandering around searching for
finance and/or
facilities in order to survive on the short term. Or
has
this collaborative behavior
(also)
been
induced by
the speed of
successive technology, service and product development
s
requiring
additional k
nowledge inputs from other organiz
ations
on short terms (e.g. Gay & Dousset,
2005)
? Consequently, one of the most intriguing questions to be addressed in future research
is not
‘W
hy
are
dyadic
collaborative relations
between DDLSFs and their partner
organi
zations established
?’
, which subject already
has
been investigated in
several
studies,
but
‘W
hy
are
dyadic
collaborative relations
between DDLSFs and their partner organizations
changed or not
?
’
And
did
this happen
for evolving reasons and in different way
s? These
latter
two questions are not only theoretically relevant in the light of the results produced by
the variance decomposition model presented before but are also
of practical relevance
for the
policies pursued by the Dutch government and other organ
izations
(including competitors)
in
order
to
address
more
effectively
the
development of
DDLSFs.
5.
Discussion and conclusions
5.1
Discussion
The variance decomposition model
of structural and temporal dynamics in
discrete non

directed
networks
explains
97%
and more
of the variance of collaborative relationships
between DDLSFs and partner organizations in the period 2002

2005. The model is
unprecedented and combines statistical methods developed in psychometrics (i.e.
correlations between normally distrib
uted continuous variables derived from dichotomous
an
d discrete data; Olsson, 1979
), sociometrics (i.e. structural and temporal c
omponents in
longitudinal data;
Wheaton et al, 1977) and economet
rics (i.e. panel data analysis;
Johnston
& DiNardo, 1997). But
the nature of the model is quite different from those utilized in
previous studies of
dyadic
collaborative behavior
and
relations
of
organizations. The latter
model
s
were
partially
specified on theoretical grounds and tested for their validity. The
varian
ce decomposition model
is a time

dependent
two

way analysis of variance model of a
graph represented as a matrix
and is specified on statistical grounds to decompose the
variance of relations into methodologically mutually exclusive components
.
The two

way
24
analysis of variance
model relates the values of the cells to the column and
row totals and the
overall total being mutually exclusive sources of variance
(see eq.2)
. As the row and column
totals correspond to the node (out)degrees of DDLSFs and the node
(in)degrees of their
partner organizations, respectively, and the overall total corresponds to the network density,
the correspondence between the graph of a network and the two

w
ay analysis of variance
model is
established. The effects of the column, row
and overall totals on the cell values
determine which part of the variance of cell values is due to each of them or cell specific (i.e.
due to unidentified
dyad
specific characteristics), which together account for all
the
variance
of cell values. However,
the column, row and overall variances and their effects and the cell
variance left over are empirical regularities to be explained themselves on theoretical
grounds (e.g. Salancik, 1995). So, the variance decomposition model helps to unravel
complex netwo
rk dynamics in order to better focus theo
ry formation
on
its largest
constituent
variance components; in other words, it increases insight into complex
empirical
network dynamics and helps to systemize (further) theory formation about it
but does not
repre
sent a theoretical model
. In this respect, Barabási & Albert’s (1999) mechanism of
preferential attachment is only a label of an empirical regularity instead of an explanati
on
(e.g. Faber & Scheper, 2003).
Another difference between the variance decomposit
ion model and earlier models of
collaborative behavior and relations is that the former model takes explicitly structural as
well as temporal effects into account whereas the latter models concentrate on structural
effects only
(e.g. Gulati & Gargiulo, 199
9; Powell et al, 2005)
. As demonstrated by the
results presented and discussed before, the temporal effects play an important role in the
evolution of collaborative behavior and relations within a rather volatile
inter

organizational
network. But in the mo
dels specified and tested in earlier studies such temporal effects and
the
effects of random errors collapse and
cannot
be separated from one another. In this
respect, the variance decomposition model
provides
important
additional information about
the dyn
amics of
inter

organizational
networks.
The differences between the data used in this study and those used in other studies have
been already discussed in Section 3.
These differences in the measurement of collaborative
relations between organizations and
their possible effects on the results obtained from data
analysis provide an interesting t
opic for further research but that
is beyond the scope of this
study.
Nevertheless,
the results obtained in this study
seem to
correspond
with
some
obtained by Powell
et al (2005) regarding the behavioral mechanisms of preferential
25
attachment and diversification applied by the most centra
l organizations
.
But in this study
both mechanisms are derived from the results obtained for one (meta) model specification of
comple
x network dynamics whereas in Powell et al
(2005)
separate models are
specified
and
tested
together
. Furthermore, the relative dominance of each mechanism can be assessed
from the results obtained in this study based on their contribution to the explained
variance
of established collaborative relations. And it can be derived whether these mechanisms are
structural or temporal in nature.
Another novelty of the data used in this study is that they seem to reflect the emergence
of the collaborative network of
DDLSFs
as a result of
the BioPartner Program of the Dutch
government since 2000. Accordingly, the results presented in this study provide specific
insight into the sources of variance and processes relevant during the
early development
of
an
industrial net
work and the industry itself, namely structural trial

and

error selection of
new firms by central established organizations and temporal
mutual
exploration of
relationships
between the most central new firms and established organizations.
Unfortunately
, th
e data used in this study cover only two periods of observation with
different le
ngths. Consequently, the effect
of changes in network density on the
establishment of collaborative relations between DDLSFs and their partner organizations
and its reducing i
mpact on the effects of other specified sources of variance could not be
estimated.
Because
the BioPartner Program ended in 2005 and data collection with it, it will
be impossible to gain insight into these effects from
future
research.
5.2
Conclusions
A
pplication of the variance decomposition model of complex network dynamics in the
analysis of inter

organizational collaborations of Dutch dedicated life sciences firms has
made clear that ‘it takes two to tango’ and that the individual network positions o
f both
collaborating organizations
in each dyad
must be explicitly taken into account. Compound
measures of these network positions and other organizational characteristics obscure the
effects inducing inter

organizational collaboration.
Additionally
, the
results show that a
distinction should be made between structural and temporal effects related to
, respectively,
th
e exploitation and exploration
of collaborative relations between organizations.
Furthermore
, as demonstrated, time series of observations of
collaborative relations between
26
organizations should be analyzed as successive waves (or windows of time) in order to gain
insight into the evolution of collaboration mechanisms structuring the network.
Although the variance decomposition model is a descr
iptive statistical model and not a
theoretical explanatory model it contributes to inter

organizational network analysis by
systemizing
‘the
groping in the dark’ and focusing theory formation on those variance
components that explain most of the variance o
f inter

organizational collaborative
relations.
Systemizing ‘
the
groping in the dark’ consists of
specifying
the possible effects of all
methodological mutually exclusive categories of sources of variation
on the (non)existence
of collaborative relations b
etween organizations
. But
significant
sources of variation
themselves need further explanation on theoretical grounds. Nevertheless, the estimates and
standard errors of the possible effects and related variance components also provide
descriptive statisti
cal insights into the dynamics of the network studied as discussed in
Sections 4.1 and 4.2
as inputs for future re
search
.
Finally, the variance decomposition model of discrete networks elevates the analysis of
(un)weighted (non)directed graphs to a multiva
riate statistical analysis. By using polychoric
correlations as measures of association between observed discrete phenomena the vast array
of
more informative
multivariate statistical methods comes available for
the analysis of
graphs
. But in every multiva
riate statistical analysis based on polychoric correlations
estimated from observed discrete network data their threefold panel data
dimensions (i.e.
i, j
and
t
)
should be taken into account.
Regarding the collaboration net
work of Dutch dedicated life scie
nces firms (DDLSFs)
the results
indicate a swift evolution of successive collaboration mechanisms
; i.e. from
dominant structural preferential attachment of DDLSFs to cent
ral partner organizations
(
Dutch universities)
and considerable
temporal mutual attach
ment between the most central
DDLSFs and partner organizations in the period 2002

2004 to dominant dyad specific
collaboration between DDLSFs and partner organizations and quite weaker temporal mutual
and structural preferential attachment mechanisms of co
llaboration in the period 2004

2005
.
This evolution of successive collaboration mechanisms occurred in the context of a very
volatile collaboration network of DDLSFs with much rewiring; of the collaborative relations
established
betw
een DDLSFs and their partner organizations
only 25% and 13% survived
after two and three years, respectively. Consequently, in order to improve our understanding
of the collaborative behavior of DDLSFs and the structuring of their collaboration network
the
question of ‘why do DDLSFs’ collaborative relation
s
change or not?’
becomes central
.
27
Future research into the
this
question will
further
improve
the
insight into the inducements
and opportunities affecting the collaborative behavior of DDLSFs
and establis
hed partner
organizations
.
28
Appendix: Polychoric correlations of the observed discrete network indicators
2002

2004 (N=23760)
0.10000D+01
0.31661D+00 0.10000D+01
0.41575D+00

0.45480D

07 0.10000D+01
0.73338D+00

0.41159D

01 0.18683D+00 0.10000D+0
1

0.37453D

01
0.16373D+00
0.44388D

07 0.32959D+00 0.10000D+01
0.12952D+00 0.25244D

07 0.22450D

01
0.48083D+00 0.10412D

06
0.10000D+01
N
ijt

1
N
i
∙
t

1
N
∙
jt

1
N
ijt
N
i
∙
t
N
∙
jt
2004

2005 (N=17184)
0.10000D+01
0.30423D+00 0.10000D+01
0.43943D+00

0.10901D

07 0.10000D+01
0.80231D+00 0.10814D

01 0.19875D+00 0.10000D+01
0.11755D

01 0.18736D

01 0.15564D

07 0.37329D+00 0.10000D+01
0.20027D+00 0.26
760D

07 0.10417D+00 0.49835D+00

0.89346D

07 0.10000D+01
N
ijt

1
N
i
∙
t

1
N
∙
jt

1
N
ijt
N
i
∙
t
N
∙
jt
legend:

N
ijt
: dichotomous relationship between DDLSF
i
and partner organization
j
in 2004 or 2005

N
i
∙
t
: node degree of DDLSF
i
in 2004 or 2005
(
t

1
= 2002
or 2004
)

N
∙
jt
: node degree of partner organization
j
in 2004
or 200
5

D

07 : * 10

7
in double precision
29
References
Ahuja, G., 2000

a. The duality of collaboration: inducements and opportunities in the formation
of interfirm linkages. Strategic Manag
ement Journal 21, 317

343.
Ahuja, G., 2000

b. Collaboration networks, structural holes and innovation: a longitudinal
study. Administrative Science Quarterly 45, 425

455.
Barabási, A.L., Albert, R., 1999. Emergence of scaling in random networks. Science 28
6, 509

512.
Baum, J.A.C., Calabrese, T., Silverman, B.S., 2000. Don’t go it alone: alliance network
composition and startups’ performance in Canadian biotechnology. Strategic
Management Journal 21, 267

294.
Clauset, A., Shalizi, C.R., Newman, M.E.J., 2007.
Power

law distributions in empirical data.
arXiv: o706.1062v1 (eprint).
Duijn, M.A.J. van, Snijders, T.A.B., Zijlstra, B.J.H., 2004.
p
2
: a random effects model with
covariates for directed graphs. Statistica Neerlandica 58, 234

254.
Eisenhardt, K.M., Scho
onhoven, C.B., 1996. Resource

based view of strategic alliance
formation: strategic and social effects in entrepreneurial firms. Organization Science 7,
136

150.
Faber, J., 1988. Consistent estimation of correlations between observed interval variables
wit
h skewed distributions. Quality and Quantity 22, 381

392.
Faber, J., Scheper, W.J., 2003. Social scientific explanations? On Quine’s legacy and
contextual fallacies. Quality & Quantity 37, 135

150.
Faber, J., Poot, T., 2009.
Multi

level auto

covariance ana
lysis
of structural and temporal
dynamics in discrete
non

directed
networks
: specification, application and extensions
.
Social Networks (
accepted
).
Fisher, F.M. 1970. A correspondence principle for simultaneous equation models.
Econometrica
38
, 73

92.
Fugu
itt, G.V., Lieberson, S., 1974. Correlations of ratios and difference scores having common
terms. In Costner, H.L. (ed). Sociological Methodology 1973

1974. Jossey

Bass, San
Francisco, 128

144.
Gay, B., Dousset, B., 2005. Innovation and network structural
dynamics: study of the alliance
network of a major sector of the biotechnology industry. Research Policy 34, 1457

1475.
30
Gulati, R., Gargiulo, M., 1999. Where do interorganizational networks come from? American
Journal of Sociology 104, 1439

1493.
Hagedoorn
, J., 1993. Understanding the rationale of strategic technology partnering: inter

organizational modes of cooperation and sectoral differences. Strategic Management
Journal 14, 371

385.
Johnston, J., DiNardo, J., 1997. Economeric methods. McGraw

Hill, New
York.
Jöreskog, K.G., Sörbom, D., 1993. LISREL
®
8: user’s reference guide. Scientific Software
International, Lincolnwood (IL).
Jöreskog, K.G., Sörbom, D., 1995. PRELIS
8: user’s reference guide. Scientific Software
International, Lincolnwood (IL).
Kenny,
D.A., La Voie, L., 1984. The social relations model. In: Berkowitz, L. (ed).
Advances in experimental social psychology (vol. 18). Academic Press: Orlando,
141

182.
Ministry of Economic Affairs, 1999. Action plan life sciences. Ministry of Economic
Affair
s, The Hague.
.
Mitzenmacher, M., 2004. A brief history of generative models for power law and lognormal
distributions. Internet Mathematics 1, 226

251.
Olsson, U., 1979. Maximum likelihood estimation of the polychoric correlation coefficient.
Psychometrika
44, 443

460.
Pfeffer, J., Salacik, G.R., 1978. The external control of organizations: a resource dependence
perspective. Harper & Row Publishers, New York.
Powell, W.W., Koput, K.W., Smith

Doerr, L., 1996. Inter

organizational collaboration and the
locus
of innovation: networks of learning in biotechnology. Administrative Science
Quarterly 41, 116

145.
Powell, W.W., White, D.R., Koput, K.W., Owen

Smith, J., 2005. Network dynamics and
field evaluation: the growth of interorganizational collaboration in the
life sciences.
American Journal of Sociology 110, 1132

1205.
Sakakibara, M., 2002. Formation of R&D consortia: industry and company effects. Strategic
Management Journal 23, 1033

1050.
Salancik, G.R., 1995. Wanted: a good network theory of organization. Ad
ministrative Science
Quarterly 40, 345

349.
31
Saris, W.E, Stronkhorst, L.H., 1984. Causal modeling in nonexperimental research.
Sociometric Research Foundation, Amsterdam.
Schweinberger, M., Snijders, T.A.B., 2007. Markov models for digraph panel data: Monte
Carlo

based derivative estimation. Computational Statistics & Data Analysis 51,
4465

4483.
Snijders, T.A.B., Kenny, D.A., 1999. The social relations model for family data: a multi

level approach. Personal Relationships 6, 471

486.
Strogatz, S.H., 2001. Ex
ploring complex networks. Nature 410, 268

276.
Stuart, T.E., 1998. Network positions and propensities to collaborate: an investigation of
strategic alliance formation in a high

technology industry. Administrative Science
Quarterly 43, 668

698.
Van der Valk
, T., Moors, E.H.M., Meeus, M.T.H., 2009. Conceptualizing patterns in the
dynamics of emerging technologies: the case of biotechnology developments in the
Netherlands. Technovation 29, 247

264.
Wheaton, B., Muthén, B., Alwin, D.F., Summer, G.F., 1977. Asse
ssing reliability and
stability in panel models. In: Heise, D.R. (ed). Sociological Methodology 1977.
Jossey Bass, San Francisco, 89

136.
Willemstein, L., Van der Valk, T., Meeus, M.T.H., 2007. Dynamics in business models: an
empirical analysis of medical
biotechnology firms in the Netherlands. Technovation 27,
221

232.
Comments 0
Log in to post a comment