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that for now we assume to be spatially bounded. The expected dynamics depend on
the net ef ect of processes that lead to more heterogeneous individual knowledge stocks
and of processes that lead to more homogeneous knowledge stocks. With levelling
factors dominating, it will become more dii cult for actors to i nd appropriate partners
within the network as the potentials to fruitfully exchange knowledge diminish. For the
structural pattern this implies the number of connections among actors will fall and/or
actors will exit the network. Actors remaining in the network are presumed to frequently
change partners; in this respect, we i nd that the likelihood of two actors cooperating in
period t is negatively af ected by cooperating in period t − 1 (Cantner and Graf, 2006).
Contrariwise, if the dynamics lead to more distinctive individual knowledge stocks,
the potentials to exchange knowledge increase. Hence, it becomes more attractive to
exchange knowledge and therefore we would expect the number of connections among
actors and/or the number of actors to increase as well. As the knowledge stocks of con-
nected actors become more similar, we would expect them to cut existing ties in favour
of new, more fruitful relationships.
These bare bones of network dynamics can now easily be extended by a geographi-
cal or regional dimension. Assuming that there are clear-cut regional boundaries to an
innovator network, one may now ask to what extent and for what reason a network may
increase its geographical reach; that is, local actors form extra-local linkages. Two main
mechanisms may be at work. The i rst one has to do with the degree of new knowledge
generation within the network. The higher this degree, the more extra-local linkages may
be formed as external actors are attracted. As explained above, in such a situation we
would also expect an increased number of internal relationships. If, contrariwise, the lev-
elling ef ect within the network dominates, actors faced by a lower potential for internal
partners may look for external relationships, or leave the network and exit. This, then,
should go hand in hand with a lower number of internal relations.
The dynamic analysis presented so far can, of course, be extended to other dimen-
sions stated above, complementarity, reciprocity and trust. For example, the levelling
ef ect may also show up as a specialization ef ect resulting in a lack of complementarity
of the knowledge bases. For that, Cantner and Graf (2004) i nd that the more a regional
network is specialized technologically, the more external relationships to exchange
knowledge are observed. Last but not least, a lack (abundance) of reciprocity as well
as of trust may lead to a lower (higher) density of relations as well as to a reduced (pro-
longed) duration of the collaboration.
For the present case study, we attempt to identify some of the dynamic patterns
introduced. We start with observations on the changing composition of the network and
of the structure of relationships within the network of innovators pertaining to Jena.
Respective changes will then subsequently be related to the role of:
potentials available for cooperation that may change over time;
permanent actors who stay within the network during the whole span of observa-
tion;
actors who enter the network presumably expecting advantages because of coop-
eration;
actors who exit the network, which may be because of a low degree of integration
and hence poor results;
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