Geoscience Reference
In-Depth Information
3. Knowledge—heterogeneous in early stages; more homogeneous in later stages
4. Entrepreneurship—greater within clusters than outside them
5. Convergence—convergence around best practices and standards increases after
the early &Mid-cycle is reached; is correlated with a change in the nature of
knowledge creation and information from heterogeneous to more homogeneous
6. Network linkages—strengthen over the life-cycle; decrease as clusters lock-in
on decline
7. Cooperation—minimal to modest initially and in the take-off and early growth
stages, increase as scale and scope increase; remain strong until lock-in on
decline progresses
12.3
Cluster Life Cycles
Figure
12.1
depicts a staged life-cycle model for clusters largely derived from
Bergman (
2008
) based on his synthesis of the work of others including Bode and
Alig (
2011
), Hassink and Shin (
2005
), Ingstrup and Damgaard (
2012
), Knop and
Olko (
2011
), Lefebvre (
2012
), Lorenzen (
2005
), Martin and Sunley (
2011
), Menzel
and Fornahl (
2009
), Sonderegger and Taube (
2010
), Swann (
2002
), and Tichy
(
1998
). This body of work evidences the recent yet sustained scholarly interest in
life-cycle theory as a framework for modeling cluster development and evolution.
General life cycle theory gained recognition from its earlier application in
business, industry and technology development (Klepper
2007
; and Utterback and
Abernathy
1975
). It assumes that a growth process has an origin, a take-off leading
to or initiating extended growth that begins at the first inflection point of the model
after which growth occurs at an increasing rate (Fig.
12.1
) but later slows after the
middle part of the cycle and rapidly declines to zero thus reaching an asymptotic
plateau. At exhaustion, cluster growth may remain in stasis but often experiences a
period of decline (locking-in on decline) or reinvention whereby a new cycle of
growth is initiated. The state of each of the seven-cluster dimensions varies as
the cluster moves through this model framework. There appears to be some
dependency among the cluster dimensions as their behavior appears to change
differentially.
While the life-cycle model implies a deterministic cluster process, it is not when
one recognizes clusters may evolve in a quite erratic fashion, may never complete
the process or the full growth cycle and/or may reach an asymptote prematurely
with respect to the model. The model thus provides a benchmark against which to
assess cluster development. Factors that may 'cause' the cluster to deviate include
physical environment events such as hurricanes, earthquakes, droughts, tornadoes,
climate change and floods. Societal or manmade factors such as business cycles,
new technology (especially radical), change in political leadership, war, post war
recovery, and conditions that eliminate competition, e.g., a region dominated by
mafia rule may induce deviation from the cluster life-cycle model.
