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The iterative nature of the analysis allows
opportunities to identify weaknesses in the
existing system early in the study and therefore
respond both through more detailed modelling and
action in the real world. An example is the
activity "monitor and control the technology
transfer process". The real world comparison
identifies that it is very desirable, but rarely
undertaken. Moreover, examination of this activity
has identified that economic evaluation of
successful remote sensing projects is an element
that to date has consistently been lacking. This
clearly identifies an action that can be taken
immediately to improve the problem situation. A
model for the economic evaluation and management of
remote sensing operations has therefore been
developed (Finegan, Ellis and Rollings, 1992) and
is now being tested.
The cyclic and adaptive nature of the SSM
facilitates learning and input from other relevant
systems models (Stage 4b in Figure 1). Therefore
comparative analysis with existing general models
for technology transfer allows the SSM model to be
both validated and further enhanced. This will
ensure that the resultant model for technology
transfer is both robust and functional.
The final model will be presented in a format that
is accessible to potential users. Options for
presentation include:
. Technology template, where the user matches
the attributes of a particular technology
with the relevant part of the template, which
then recommends actions to improve the
situation.
Decision tree, where, based on the operation
of rules, the user traverses the tree to a
set of recommended actions. This mode lends
itself to implementation on a rules based
expert system for remote sensing technology
management (Finegan 1992).
CONCLUSION
The Soft Systems Methodology described in this
study is successfully providing action to improve
the situation for the users of remote sensing,
while developing a model for technology transfer
that will be both useful and accessible to the
wider Australian community.
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