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Knowledge Management Models 87

3. Solving problems
4. Making decisions
5. Taking actions to achieve desired results
Since only people or individuals can make decisions and take actions, the emphasis
of this model is on the individual knowledge worker and his or her competency,
capacity, learning, and so on. These are leveraged through multiple networks (e.g.,
communities of practice) to make available the knowledge, experience, and insights
of others. This type of tacit knowledge leveraged through dynamic networks makes a
broader “ highway ” available to connect data, information, and people through virtual
communities and knowledge repositories.
To survive and successfully compete, an organization will also require eight emer-
gent characteristics, according to this model:

1. Organizational intelligence
2. Shared purpose
3. Selectivity
4. Optimum complexity
5. Permeable boundaries
6. Knowledge centricity
7. Flow
8. Multidimensionality
An emergent characteristic is the result of nonlinear interactions, synergistic inter-
actions, and self-organizing systems. The ICAS KM model follows along the lines of
the other approaches in that it is connectionist and holistic in nature. The emergent
ICAS characteristics are outlined in fi gure 3.11 . These emergent properties serve to
endow the organization with the internal capability to deal with the future unantici-
pated environments yet to be encountered.
Organizational intelligence refers to the capacity of the fi rm to innovate, acquire
knowledge, and apply that knowledge to relevant situations. In the ICAS model, this
property refers to the ability of the organization to perceive, interpret, and respond
to its environment in such a way as to meet its goals and satisfy its stakeholders.
This is very similar to the Choo sense-making model approach. Unity and a shared
purpose represent the ability of the organization to integrate and mobilize resources
through a continuous, two-way communication with its large number of relatively
independent subsystems, much like the VSM. Optimum complexity represents
the right balance between internal complexity (i.e., the number of different relevant