Why Knowledge Management Systems Fail

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Author Malhotra 2004

The thesis is that enablers of KMS unravel and become constraints in adapting and evolving for business environments characterized as high uncertainty. KMS must be in alignment. Having multiple trajectories and human inputs embedded in them can reduce the risk of obsolescence

  • Often moderating and intervening variables may play a role skewing simplistic relationships
  • Original ideas of KMS were reified from thinking that IT would play a bigger role than human imputs in the performance outcome
  • Many business have been adopting technologies for stroing employees knowledge with mixed results
  • Gartner (1998) KM promotes an integrated approach for identifying, capturing, retrieving, sharing, evaluating info assets. can be stored in db's docs, policies procedures, as well as uncaptured tacit expertise stored in individual's heads
  • Such inputs oriented mechanistic and statis representations of K do provides hints as to how they affect business perf or how to deal with emotions of specific contexts
  • Mangement focus has tended to be on best practices and knowledge re-use rather than creation of new knowledge. Info and Knowledge are seen as synonymous constructs
  • Limited scope for diverse interpreations or a multiplicity of meanings. The model is supposed to be pre-specified with kimited feedback loops for fine tuning outcomes - not godd in uncertain environments.

KMS Model 2 for non-routine sense makin

Seen as intelligence in aciton using interaction of data, information rules, traits such as motivation etc. It is active, affective and dynamic because it is being used in action, takes in to account emotinal dimensions and dynamic based on reinterpreation of data.

Landau and Stout = Knowledge resides in the user not in the collection Nonaka - Knowledge is jusified belief and commitment unlike information

Implications

  • Internet and web is an element of the strategy not THE stratgegy to produce value
  • the IT must be up to the job and take into account motivations etc
  • need to create adaptive IT capacity
  • loose couplings between technology architectures and business architectures
  • Scalable
  • integration across and outside the enterprise but requires the stakeholders to trust each other
  • Often individuals will not share information believing it provides them an inherent advantage and often result in sharing ambiguous, innaccurate or partial information
  • employees may know more than they think they know
  • self organizing ecosystems may help
  • Oragimizational controls focus on error avoiodance and single loop learning
  • Price waterhouse coopers have Knowledge Curve a mixture of structured and routine data
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