Knowledge management (KM) is the process of capturing, developing, sharing, and effectively using organisational knowledge. It refers to a multi-disciplined approach to achieving organisational objectives by making the best use of knowledge.
An established discipline since 1991 (see Nonaka 1991), KM includes courses taught in the fields of business administration, information systems, management, and library and information sciences (Alavi & Leidner 1999). More recently, other fields have started contributing to KM research; these include information and media, computer science, public health, and public policy. Columbia University and Kent State University offer dedicated Master of Science degrees in Knowledge Management.
Many large companies and non-profit organisations have resources dedicated to internal KM efforts, often as a part of their business strategy, information technology, or human resource management departments. Several consulting companies provide strategy and advice regarding KM to these organisations.
Knowledge management efforts typically focus on organisational objectives such as improved performance, competitive advantage, innovation, the sharing of lessons learned, integration and continuous improvement of the organisation. KM efforts overlap with organisational learning and may be distinguished from that by a greater focus on the management of knowledge as a strategic asset and a focus on encouraging the sharing of knowledge. It is seen as an enabler of organisational learning and a more concrete mechanism than the previous abstract research.
Knowledge management efforts have a long history, to include on-the-job discussions, formal apprenticeship, discussion forums, corporate libraries, professional training and mentoring programs. With increased use of computers in the second half of the 20th century, specific adaptations of technologies such as knowledge bases, expert systems, knowledge repositories, group decision support systems, intranets, and computer-supported cooperative work have been introduced to further enhance such efforts.
In the enterprise, early collections of case studies recognized the importance of knowledge management dimensions of strategy, process, and measurement. Key lessons learned include people and the cultural norms which influence their behaviors are the most critical resources for successful knowledge creation, dissemination, and application; cognitive, social, and organisational learning processes are essential to the success of a knowledge management strategy; and measurement, benchmarking, and incentives are essential to accelerate the learning process and to drive cultural change. In short, knowledge management programs can yield impressive benefits to individuals and organisations if they are purposeful, concrete, and action-oriented.
More recently with the advent of the Web 2.0, the concept of knowledge management has evolved toward a vision more based on people participation and emergence. This line of evolution is termed Enterprise 2.0. However, there is an ongoing debate and discussions as to whether Enterprise 2.0 is a fad that does not bring anything new or useful or whether it is, indeed, the future of knowledge management.
KM emerged as a scientific discipline in the earlier 1990s. It was initially supported solely by practitioners, when Skandia hired Leif Edvinsson of Sweden as the world's first Chief Knowledge Officer (CKO). Hubert Saint-Onge (formerly of CIBC, Canada), started investigating KM long before that. The objective of CKOs is to manage and maximize the intangible assets of their organisations. Gradually, CKOs became interested in practical and theoretical aspects of KM, and the new research field was formed. Discussion of the KM idea has been taken up by academics, such as Ikujiro Nonaka (Hitotsubashi University), Hirotaka Takeuchi (Hitotsubashi University), Thomas H. Davenport (Babson College) and Baruch Lev (New York University). In 2001, Thomas A. Stewart, former editor at Fortune magazine and subsequently the editor of Harvard Business Review, published a cover story highlighting the importance of intellectual capital in organisations. Since its establishment, the KM discipline has been gradually moving towards academic maturity. First, there is a trend toward higher cooperation among academics; particularly, there has been a drop in single-authored publications. Second, the role of practitioners has changed. Their contribution to academic research has been dramatically declining from 30% of overall contributions up to 2002, to only 10% by 2009 (Serenko et al. 2010).
A broad range of thoughts on the KM discipline exist; approaches vary by author and school. As the discipline matures, academic debates have increased regarding both the theory and practice of KM, to include the following perspectives:
- Techno-centric with a focus on technology, ideally those that enhance knowledge sharing and creation.
- Organisational with a focus on how an organisation can be designed to facilitate knowledge processes best.
- Ecological with a focus on the interaction of people, identity, knowledge, and environmental factors as a complex adaptive system akin to a natural ecosystem.
Regardless of the school of thought, core components of KM include people, processes, technology (or) culture, structure, technology, depending on the specific perspective (Spender & Scherer 2007). Different KM schools of thought include lenses through which KM can be viewed and explained, to include:
- community of practice 
- social network analysis
- intellectual capital (Bontis & Choo 2002)
- information theory (McInerney 2002)
- complexity science
- constructivism(Nanjappa & Grant 2003)
The practical relevance of academic research in KM has been questioned (Ferguson 2005) with action research suggested as having more relevance (Andriessen 2004) and the need to translate the findings presented in academic journals to a practice (Booker, Bontis & Serenko 2008).
Different frameworks for distinguishing between different 'types of' knowledge exist. One proposed framework for categorizing the dimensions of knowledge distinguishes between tacit knowledge and explicit knowledge. Tacit knowledge represents internalized knowledge that an individual may not be consciously aware of, such as how he or she accomplishes particular tasks. At the opposite end of the spectrum, explicit knowledge represents knowledge that the individual holds consciously in mental focus, in a form that can easily be communicated to others. (Alavi & Leidner 2001). Similarly, Hayes and Walsham (2003) describe content and relational perspectives of knowledge and knowledge management as two fundamentally different epistemological perspectives. The content perspective suggest that knowledge is easily stored because it may be codified, while the relational perspective recognizes the contextual and relational aspects of knowledge which can make knowledge difficult to share outside of the specific location where the knowledge is developed.
Early research suggested that a successful KM effort needs to convert internalized tacit knowledge into explicit knowledge to share it, and the same effort must permit individuals to internalize and make personally meaningful any codified knowledge retrieved from the KM effort. Subsequent research into KM suggested that a distinction between tacit knowledge and explicit knowledge represented an oversimplification and that the notion of explicit knowledge is self-contradictory. Specifically, for knowledge to be made explicit, it must be translated into information (i.e., symbols outside of our heads) (Serenko & Bontis 2004). Later on, Ikujiro Nonaka proposed a model (SECI for Socialization, Externalization, Combination, Internalization) which considers a spiraling knowledge process interaction between explicit knowledge and tacit knowledge (Nonaka & Takeuchi 1995). In this model, knowledge follows a cycle in which implicit knowledge is 'extracted' to become explicit knowledge, and explicit knowledge is 're-internalized' into implicit knowledge. More recently, together with Georg von Krogh, Nonaka returned to his earlier work in an attempt to move the debate about knowledge conversion forwards (Nonaka & von Krogh 2009).
A second proposed framework for categorizing the dimensions of knowledge distinguishes between embedded knowledge of a system outside of a human individual (e.g., an information system may have knowledge embedded into its design) and embodied knowledge representing a learned capability of a human body’s nervous and endocrine systems (Sensky 2002).
A third proposed framework for categorizing the dimensions of knowledge distinguishes between the exploratory creation of "new knowledge" (i.e., innovation) vs. the transfer or exploitation of "established knowledge" within a group, organisation, or community. Collaborative environments such as communities of practice or the use of social computing tools can be used for both knowledge creation and transfer.
Knowledge may be accessed at three stages: before, during, or after KM-related activities. Organisations have tried knowledge capture incentives, including making content submission mandatory and incorporating rewards into performance measurement plans. Considerable controversy exists over whether incentives work or not in this field and no consensus has emerged.
One strategy to KM involves actively managing knowledge (push strategy). In such an instance, individuals strive to explicitly encode their knowledge into a shared knowledge repository, such as a database, as well as retrieving knowledge they need that other individuals have provided to the repository. This is commonly known as the Codification approach to KM.
Another strategy to KM involves individuals making knowledge requests of experts associated with a particular subject on an ad hoc basis (pull strategy). In such an instance, expert individual(s) can provide their insights to the particular person or people needing this (Snowden 2002). This is commonly known as the Personalisation approach to KM.
Hansen et al. propose a simple framework, distinguishing two opposing KM strategies: codification and personalization. Codification focuses on collecting and storing codified knowledge in previously designed electronic databases to make it accessible to the organisation. Codification can therefore refer to both tacit and explicit knowledge. In contrast, the personalization strategy aims at encouraging individuals to share their knowledge directly. Information technology plays a less important role, as it is only supposed to facilitate communication and knowledge sharing among members of an organisation.
- Rewards (as a means of motivating for knowledge sharing)
- Storytelling (as a means of transferring tacit knowledge)
- Cross-project learning
- After action reviews
- Knowledge mapping (a map of knowledge repositories within a company accessible by all)
- Communities of practice
- Expert directories (to enable knowledge seeker to reach to the experts)
- Best practice transfer
- Knowledge fairs
- Competence management (systematic evaluation and planning of competences of individual organisation members)
- Proximity & architecture (the physical situation of employees can be either conducive or obstructive to knowledge sharing)
- Master-apprentice relationship
- Collaborative technologies (groupware, etc.)
- Knowledge repositories (databases, bookmarking engines, etc.)
- Measuring and reporting intellectual capital (a way of making explicit knowledge for companies)
- Knowledge brokers (some organisational members take on responsibility for a specific "field" and act as first reference on whom to talk about a specific subject)
- Social software (wikis, social bookmarking, blogs, etc.)
- Inter-project knowledge transfer
- Making available increased knowledge content in the development and provision of products and services
- Achieving shorter new product development cycles
- Facilitating and managing innovation and organisational learning
- Leveraging the expertise of people across the organisation
- Increasing network connectivity between internal and external individuals
- Managing business environments and allowing employees to obtain relevant insights and ideas appropriate to their work
- Solving intractable or wicked problems
- Managing intellectual capital and intellectual assets in the workforce (such as the expertise and know-how possessed by key individuals)
Debate exists whether KM is more than a passing fad, though increasing amount of research in this field may help to answer this question, as well as create consensus on what elements of KM help determine the success or failure of such efforts (Wilson 2002). Knowledge sharing remains a challenging issue for knowledge management, while there is no clear agreement barriers may include time issues for knowledge works, the level of trust, lack of effective support technologies and culture (Jennex 2008).
Early KM technologies included online corporate yellow pages as expertise locators and document management systems. Combined with the early development of collaborative technologies (in particular Lotus Notes), KM technologies expanded in the mid-1990s. Subsequent KM efforts leveraged semantic technologies for search and retrieval and the development of e-learning tools for communities of practice (Capozzi 2007). Knowledge management systems can thus be categorized as falling into one or more of the following groups: Groupware, document management systems, expert systems, semantic networks, relational and object oriented databases, simulation tools, and artificial intelligence
More recently, development of social computing tools (such as bookmarks, blogs, and wikis) have allowed more unstructured, self-governing or ecosystem approaches to the transfer, capture and creation of knowledge, including the development of new forms of communities, networks, or matrixed organisations. However such tools for the most part are still based on text and code, and thus represent explicit knowledge transfer. These tools face challenges in distilling meaningful re-usable knowledge and ensuring that their content is transmissible through diverse channels(Andrus 2005).
Software tools in knowledge management are a collection of technologies and are not necessarily acquired as a single software solution. Furthermore, these knowledge management software tools have the advantage of using the organisation's existing information technology infrastructure. Organisations and business decision makers spend a great deal of resources and make significant investments in the latest technology, systems and infrastructure to support knowledge management. It is imperative that these investments are validated properly, made wisely and that the most appropriate technologies and software tools are selected or combined to facilitate knowledge management.
Knowledge management has become a cornerstone in emerging business strategies such as Service Lifecycle Management (SLM) with companies increasingly turning to software vendors to enhance their efficiency in industries including, but not limited to, the aviation industry.
- Knowledge community
- Knowledge Discovery Process
- Knowledge ecosystem
- Database versus Data warehouse
- Knowledge engineering
- Data Mining Process
- Knowledge management software
- Support and Confidence
- Decision Tree Algorithm and Examples
- Knowledge transfer
- Knowledge sharing System
- Knowledge Cafe
- Tacit Knowledge versus Explicit Knowledge
- Ignorance management
- Legal case management
- Knowledge modeling
- Nanaka & Tekeuchi Model
- Electronic Journal of Knowledge Management
- Journal of Knowledge Management
- Journal of Knowledge Management Practice
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