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Knowledge management (KM) may referZorn, Theodore E. and Taylor, James R. Knowledge management and/as organizational communication in: Zorn and Taylor (pp 98-99) distinguish four uses of the term "knowledge management". to the ways organizations gather, manage, and use the knowledge that they acquire.

The term also designates an approach to improving organizational outcomes and organizational learning by introducing into an organization a range of specific processes and practices for identifying and capturing knowledge, know-how, expertise and other intellectual capital, and for making such knowledge assets available for transfer and reuse across the organization.

Knowledge management programs are typically tied to specific organizational objectives and are intended to lead to the achievement of specific targeted results such as improved performance, competitive advantage, or higher levels of innovation.

While knowledge transfer (an aspect of KM) has always existed in one form or another, for example through on-the-job discussions with peers, formally through apprenticeship, professional training and mentoring programmes, and — since the late twentieth century — technologically through knowledge bases, expert systems, and other knowledge repositories, KM programs seek to consciously evaluate and manage the process of accumulation and application of intellectual capital. KM has therefore brought together various strands of thought and practice relating to:

While Knowledge Management programs are closely related to Organizational Learning initiatives, Knowledge Management may be differentiated from Organizational Learning by its greater focus on the management of specific knowledge assets.

The rise of KM has seen an increasing understanding of the relevance of the distinction between tacit and explicit knowledge, sophisticated perspectives on the management, assessment and use of intellectual capital, and the emergence of new organizational roles and responsibilities such as the position of Chief Knowledge Officer (CKO).

Key concepts in KM

Tacit versus explicit knowledge

A key distinction made by KM practitioners is between tacit and explicit knowledge. Some scholars add one more category, implicit knowledge, but most presume there are two kinds. The former is often subconscious and internalised, and individuals may or may not be aware of what they know and how they accomplish particular results. At the opposite end of the spectrum is explicit knowledge — this refers to knowledge that individuals hold explicitly and consciously in mental focus, and may communicate to others, and especially to such knowledge when codified into written or another permanent form.

Note that there is a partial inconsistency between the definition of tacit and explicit knowledge, and the definition that knowledge is information of which a person, organization or other entity is aware. Such inconsistency arises from the necessity to manage knowledge in organizations, originating an overlap between the concept of knowledge and that one of information. For example, explicit knowledge can be stored in systems (knowledge bases), but this is a contractdition of terms if we assume that knowledge has no isolated existence, that is, there has to be a person who wants to know.

Nonaka and Takeuchi argued that on the one hand, a successful KM program needs to convert internalised tacit knowledge into explicit codified knowledge in order to share it, but on the other hand, individuals and groups must also internalise and make personally meaningful explicit knowledge once they retrieve it from its codified form (e.g., in a KM system).

Critics have however argued that Nonaka and Takeuchi's distinction between tacit and explicit knowledge is oversimplified, and even that the notion of explicit knowledge is self-contradictory.Zorn and Taylor, op. cit., p104.

A third kind of knowledge is embedded knowledge. Embedded knowledge is a knowledge that is embedded in a physical object but not in a explicit way, that is, it requires other knowledge to be extracted. For example, the shape and characteristics of a unknown device contains the key elements to understand how that device can be used.

Knowledge capture stages

Knowledge may be accessed, or captured, at three stages: before, during, or after knowledge-related activities.

For example, individuals undertaking a new project for an organization might access KM resources to learn best practices and lessons learned for similar projects undertaken previously, access the KM network again during the project implementation to seek advice on issues encountered, and access the system afterwards for advice on after-project actions and review activities.

Similarly, knowledge may be captured and recorded into the system before the project implementation, for example as the project team learns information and lessons during the initial project analysis. Similarly, lessons learned during the project operation may be entered into the KM system, and after-action reviews may lead to further insights and lessons being recorded in the KM system for future access.

Ad hoc knowledge access

One alternative strategy to encoding knowledge into and retrieving knowledge from a knowledge repository such as a database is for individuals to instead access expert individuals on an ad hoc basis, as needed, with their knowledge requests. A key benefit of this strategy is that the response from the expert individual is rich in content and contextualized to the particular problem being addressed and personalised to the particular person or people addressing it. The downside is, of course, that it is tied to the availability of specific individuals in the organization, and does not capture their insights and experience for future use should they leave or become unavailable, and that expert's memories of particular technical issues or problems previously faced may change with time.

Drivers of KM

There are a number of 'drivers', or motivations, leading to organizations undertaking a Knowledge Management program.

Perhaps first among these is to gain the competitive advantage that comes with improved or faster learning and new knowledge creation. KM programs may lead to greater innovation, better customer experiences, consistency in best practices and knowledge access across a global organization, as well as many other benefits, and KM programs may be driven with these goals in mind.

Considerations driving a knowledge management program might include:

  • making available increased knowledge content in the development and provision of products and services
  • achieving shorter new product development cycles
  • facilitating and managing organisational innovation
  • leverage the expertise of people across the organization
  • Benefiting from 'network effects' as the number of productive connections between employees in the organization increases and the quality of information shared increases
  • managing the proliferation of data and information in complex business environments and allowing employees to rapidly access useful and relevant knowledge resources and best practice guidelines
  • facilitate organizational learning
  • managing intellectual capital and intellectual assets in the workforce (such as the expertise and know-how possessed by key individuals) as individuals retire - in larger numbers than they have in a long time - and new workers are hired

KM enablers

Historically, there have been a number of technologies 'enabling' or facilitating KM practices in the organization, including expert systems, knowledge bases, software help desk tools, document management systems and other IT systems supporting organizational knowledge flows.

The advent of the internet brought with it further enabling technologies, including E-learning, web conferencing, collaborative software, Content management systems, corporate 'Yellow pages' directories, email lists, Wikis, Blogs, and other technologies. Each enabling technology can expand the level of inquiry available to an employee, while providing a platform to achieve specific goals or actions. The practice of KM will continue to evolve with the growth of collaboration applications available by IT and through the Internet.Since its adoption by the mainstream population and business community, the Internet has led to an increase in creative collaboration, learning and research, e-commerce, and instant information.

There are also a variety of organizational enablers for KM programs, including Communities of Practice, before-, after- and during- action reviews (see After Action Review), peer assists, information taxonomies, coaching and mentoring, and so on.

KM roles and organizational structure

Knowledge Management activities may be centralised in a Knowledge Management Office (KMO), or responsibility for Knowledge Management may be located in existing departmental functions, such as the HR or IT departments.

However, many of the more successful Knowledge Management initiatives have begun in more limited, tactical areas such as customer or end user support, where metrics for success are easily quantifiable.

Organizations committed to Knowledge Management on an ongoing basis may create a specific position such as a Chief Knowledge Officer (CKO) or similar, or assign responsibilities for management of a Knowledge Management program to a specific relevant organizational department (e.g. to Human Resources to manage intellectual capital, to IT to manage IT systems for Content Management and Document Management, etc.)

KM lexicon

KM professionals may use a specific lexicon in order to articulate and discuss the various issues arising in Knowledge Management. For example, terms such as intellectual capital, metric, and tacit vs explicit knowledge typically form an indispensable part of the KM professional's vocabulary.

2nd Generation KM

Mark W. McElroy has defined a generational approach to understanding KM.

First generation concepts of KM centre around people sharing knowledge with each other. Knowledge could consist of ideas, documents, emails, media and etc. This idea fueled the software industry to market products such as document management systems (DMSs) where members of an organization could post their knowledge objects for others to access.

Although knowledge sharing is an essential piece of the puzzle for KM, McElroy points out it is not the end by any means. Knowledge sharing alone does not lead to innovation. Organizations who only share knowledge often get locked up in stagnant knowledge that needs refining. Organizations today, more than ever, need to be fluid and proactive to market trends. Knowledge sharing only lets an organization maintain not progress. Second generation KM focuses on knowledge creation, a social process that can be enabled with software solutions. Knowledge creation starts with knowledge sharing. A contributor brings an idea to the table and members with their organization take that knowledge through a cycle of feedback and refinement. Software products such as a wiki support this type of approach.

This process is known as the Knowledge Management Life Cycle. The model, while although complex diagrammatically, is quite simple. A person thinks up of an idea. Most likely this idea is in an imperfect, context-specific state. The idea is shared among colleagues. The colleagues analyze the idea and offer their opinions, experiences and general feedback. The idea is revised based on this feedback. The result is a more perfected, less context-specific knowledge object. The process result is knowledge creation, not simply knowledge sharing. Traditional software solutions such as databases and knowledge bases do not support this process well as once a knowledge object is posted, it stagnates and never evolves. Wikis and other collaborative software solutions such as instant messaging, forums, and video conferencing are more able to support the KM Life Cycle.

See also Human-based genetic algorithm

Related definitions

See also

Notes

Further reading

  • Dixon, N. M. (2000), Common Knowledge: How Companies Thrive by Sharing What They Know, Harvard Business School Press, Boston, MA.
  • Becerra-Fernandez, I., A. González and R. Sabherwal (2004), Knowledge Management (1st Edition), Upper Saddle River, New Jersey: Pearson Education, Inc., 2004
  • Garvin, D. A. (2000), Learning in Action: A Guide to Putting the Learning Organization to Work, Harvard Business School Press, Boston, MA.
  • Easterby-Smith, M. and M. A. Lyles (editors). (2003). The Blackwell Handbook of Organizational Learning and Knowledge Management, Oxford, Blackwell Publishing.
  • Hansen, M. R., N. Nohria and T. Tierney (1999). 'What's your strategy for managing knowledge?' Harvard Business Review (March-April).
  • Malhotra, Y. (2000), Knowledge Management and Virtual Organizations, Idea Group Publishing, Hershey, PA.
  • Malhotra, Y. (2001), Knowledge Management and Business Model Innovation, Idea Group Publishing, Hershey, PA.
  • Frid, R. (2004), Frid Framework for Enterprise Knowledge Management, IUniverse Publishing, ISBN 0-595-30699-3
  • O'Dell, C., C. J. Grayson Jr. and N. Essaides (1998), If Only We Knew What We Know: The Transfer of Internal Knowledge and Best Practice, Free Press, New York.
  • Polanyi, M. (1967), The Tacit Dimension, Doubleday, Garden City, N.Y.
  • Powell, J and Swart, J (2005) "This is what the fuss is about"- a systemic modeling for organizational knowing , Journal of Knowledge Management Vol . 9 no. 2 pp 45-58
  • Powell, J and Swart, J (2005) "Men and Measures" - capturing knowledge requirement in firms through qualitative system modeling,Journal of operational Research.
  • Rumizen, M. C. (2001), Complete Idiot's Guide to Knowledge Management, Alpha.
  • Suresh, J. K. and Mahesh, K. (2006), Ten Steps to Maturity in Knowledge Management: Lessons in Economy, Chandos, Oxford, UK.
  • Swart, J (2006) "Intellectual Capital" : Disentangling an enigmatic concept, Journal of Intellectual Capital Vol 7 No 2 pp 136-159 .
  • Tiwana, A. (2002), The Knowledge Management Toolkit: Orchestrating IT, Strategy, and Knowledge Platforms (2nd Edition), Upper Saddle River, NJ: Prentice Hall, 2002.

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