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84 Chapter 3

Table 3.3
The social learning cycle in Boisot ’ s I-Space KM model

Phase Name Description
1 Scanning • Identifying threats and opportunities in generally available
but often fuzzy content
• Scanning patterns such as unique or idiosyncratic insights
that then become the possession of individuals or small
groups
• Scanning may be very rapid when the data is well codifi ed
and abstract and very slow and random when the data is
uncodifi ed and context-specifi c
2 Problem solving • The process of giving structure and coherence to such
insights, that is, codifying them
• In this phase they are given a defi nite shape and much of
the uncertainty initially associated with them is eliminated
• Problem solving initiated in the uncodifi ed region of the
I-Space is often both risky and confl ict-laden
3 Abstracting • Generalizing the application of newly codifi ed insights to a
wider range of situations
• Involves reducing them to their most essential features, that
is, conceptualizing them
• Problem solving and abstraction often work in tandem
4 Diffusing • Sharing the newly created insights with a target population
• The diffusion of well codifi ed and abstract content to a large
population will be technically less problematic than that of
content which is uncodifi ed and context-specifi c
• Only a sharing of context by sender and receiver can speed
up the diffusion of uncodifi ed data
• The probability of a shared context is inversely proportional
to population size
5 Absorbing • Applying the new codifi ed insights to different situations in
a “ learning by doing ” or a “ learning by using ” fashion
• Over time, such codifi ed insights come to acquire a
penumbra of uncodifi ed knowledge which helps to guide
their application in particular circumstances
6 Impacting • The embedding of abstract knowledge in concrete practices
• The embedding can take place in artifacts, technical or
organizational rules, or in behavioral practices
• Absorption and impact often work in tandem
Source: Adapted from Boisot (1998).