Knowledge management

Knowledge management (KM) comprises a range of practices used in an organisation to identify, create, represent, distribute and enable adoption of insights and experiences. Such insights and experiences comprise knowledge, either embodied in individuals or embedded in organisational processes or practice.

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 Template:Harv. More recently, other fields have started contributing to KM research; these include information and media, computer science, public health, and public policy.

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 Template:Harv. Several consulting companies also exist that provide strategy and advice regarding KM to these organisations.

KM efforts typically focus on organisational objectives such as improved performance, competitive advantage, innovation, the sharing of lessons learned, 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. KM efforts can help individuals and groups to share valuable organisational insights, to reduce redundant work, to avoid reinventing the wheel per se, to reduce training time for new employees, to retain intellectual capital as employees turnover in an organisation, and to adapt to changing environments and markets Template:Harv Template:Harv.

History

KM efforts have a long history, to include on-the-job discussions, formal apprenticeship, discussion forums, corporate libraries, professional training and mentoring programs. More recently, 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 1999, the term personal knowledge management was introduced which refers to the management of knowledge at the individual level Template:Harv.

In terms of the enterprise, early collections of case studies recognized the importance of knowledge management dimensions of strategy, process, and measurement Template:Harv. Key lessons learned included: people, and the cultures that influence their behaviors, are the single most critical resource for successful knowledge creation, dissemination, and application; cognitive, social, and organizational 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 organizations 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 towards a vision more based on people participation and emergence. This line of evolution is termed Enterprise 2.0 Template:Harv. However, there is an ongoing debate and discussions Template:Harv as to whether Enterprise 2.0 is just a fad that does not bring anything new or useful or whether it is, indeed, the future of knowledge management Template:Harv.

Knowledge management as an academic discipline

KM emerged as a scientific discipline in the earlier 1990s. It was initially supported by only practitioners, when Scandia hired Leif Edvinsson of Sweden as the world’s first Chief Knowledge Officer (CKO). Hubert Saint-Onge (formerly of CIBC, Canada), started investigating various sides of KM long before that. The objective of CKOs is to manage and maximize the intangible assets of their organizations. Gradually, CKOs became interested in not only practical but also theoretical aspects of KM, and the new research field was formed. The KM ideas were quickly endorsed by several highly regarded 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 Stewart, former editor at FORTUNE Magazine, published an excellent cover story highlighting the importance of intellectual capital of organizations Template:Harv.

After that, the KM discipline has started quickly evolving. Serenko and Bontis, in their meta-analysis of KM research predicted that the total number of KM works would exceed 10,000 by 2010 Template:Harv. In fact, this number has quickly grew much faster. As of 2009, there were 20 distinct KM academic journals available Template:Harv, with Journal of Knowledge Management and Journal of Intellectual Capital ranked as the leading A+ pure-KM outlets Template:Harv. Dozens of national and international conferences were held with McMaster World Congress on the Management of Intellectual Capital and Innovation being the pioneering event Template:Harv. A number of KM research centers were formed (e.g., The Monieson Centre, Queen’s University and Knowledge Management Research Centre, Hong Kong Polytechnic University). Graduate-level university courses were introduced since 2001 Template:Harv Template:Harv.

Recently, a comprehensive scientometric analysis of the entire KM discipline was undertaken Template:Harv. It was found that KM researchers tend to adapt methods of inquiry from reference disciplines, mostly from accounting, finance, human resources management, organizational behavior, psychology, and information systems. The methods of inquiry employed by KM researchers are: 1) framework, model, approach, principle, index, metrics, or tool development (32%); 2) case study (24%); 3) literature review (work based on existing literature) (11%); 4) survey (10%); and 5) use of secondary data (8%). Other methods, for instance, focus groups or field experiments are very rare in KM research. The most productive KM countries are USA, UK, Australia, Spain and Canada that generated over 50% of the word’s KM research output, with 21% coming solely from USA. The leading research institutions are Cranfield University, UK; Copenhagen Business School, Denmark; Macquarie University, Australia; University of Oviedo, Spain; and McMaster University, Canada. It was concluded that KM research may potentially contribute to the wealth of nations because the correlation between countries’ GDP per capita and their KM scholarly research output is strong (Spearman’s pho = 0.597, p < 0.000).

Since its establishment, the KM discipline has been gradually moving towards academic maturity. First, there is a trend towards 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. At the same time, this phenomenon is regrettable since academics may lose touch with practice and start producing research that is of less interest to industry professionals. In fact, the issue of relevance of academic research has been frequently raised in all fields, including KM. A series of interviews with a number of KM managers revealed that KM research is highly relevant to the needs of practice. However, there should be effective and efficient mechanisms to translate the findings presented in academic journals to a more comprehensible format accessible to non-academics Template:Harv.

Research

A broad range of thoughts on the KM discipline exists with no unanimous agreement; 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 Template:Harv. Different KM schools of thought include various lenses through which KM can be viewed and explained, to include:

• community of practice Template:Harv
• social network analysis
• intellectual capital Template:Harv
• information theory Template:Harv
• complexity science
• constructivism Template:Harv

Dimensions

Different frameworks for distinguishing between knowledge exist. One proposed framework for categorising the dimensions of knowledge distinguishes between tacit knowledge and explicit knowledge. Tacit knowledge represents internalised 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. Template:Harv.

Early research suggested that a successful KM effort needs to convert internalised tacit knowledge into explicit knowledge in order to share it, but the same effort must also permit individuals to internalise 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) Template:Harv. 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 Template:Harv. In this model, knowledge follows a cycle in which implicit knowledge is 'extracted' to become explicit knowledge, and explicit knowledge is 'reinternalised' into implicit knowledge.

A second proposed framework for categorising 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 Template:Harv.

A third proposed framework for categorising 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 .

Strategies

Knowledge may be accessed at three stages: before, during, or after KM-related activities. Different organisations have tried various 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 also 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 Template:Harv. This is also commonly known as the Personalization approach to KM.

Other knowledge management strategies for companies include:

• 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
• competence management (systematic evaluation and planning of competences of individual organization 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 organizational 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)

Particularly, the implementation of formal knowledge management practices is important in large organizations. When the number of employees exceeds 150, internal knowledge sharing dramatically decreases because of higher complexity in the formal organizational structure, weaker inter-employee relationships, lower trust, reduced connective efficacy, and less effective communication. As such, as the size of an organizational unit increases, the effectiveness of internal knowledge flows dramatically diminishes and the degree of intra-organizational knowledge sharing decreases Template:Harv.

Motivations

A number of claims exist as to the motivations leading organisations to undertake a KM effort . Typical considerations driving a KM effort include:

• 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 organizational learning
• Leveraging the expertise of people across the organization
• 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 hopefully help to answer this question, as well as create consensus on what elements of KM help determine the success or failure of such efforts Template:Harv .

Technologies

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 Template:Harv.

More recently, development of social computing tools (such as 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 Template:Harv.

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. A set of characteristics that should support decision makers in the selection of software tools for knowledge management are available .

Knowledge management has also 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.

See also

• Chief knowledge officer
• Community of practice
• Competitive intelligence
• Complexity theory and organizations
• Computer supported cooperative work
• Collective intelligence
• Collective unconscious
• Concept map
• Data mining
• DIKW
• Enterprise content management
• Enterprise 2.0
• Enterprise bookmarking
• Enterprise social software
• Expert system
• Explicit knowledge
• Human-computer interaction
• Information ecology
• Knowledge
• Knowledge base
• Knowledge economy
• Knowledge ecosystems

• Knowledge engineering
• Knowledge management software
• Knowledge market
• Knowledge representation
• Knowledge tagging
• Knowledge transfer
• Knowledge worker
• Knowledge-based theory of the firm
• Management information system
• Metaknowledge
• Ontology
• Organisational memory
• Personal information management
• Personal knowledge management
• Sensemaking
• Semantic web
• Social network
• Sociology of knowledge
• Tacit knowledge
• Value network analysis

References

• Addicott, Rachael; McGivern, Gerry; Ferlie, Ewan (2006). "Networks, Organizational Learning and Knowledge Management: NHS Cancer Networks". Public Money & Management 26 (2): 87-94. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=889992. 
• Alavi, Maryam; Leidner, Dorothy E. (1999). "Knowledge management systems: issues, challenges, and benefits". Communications of the AIS 1 (2). http://portal.acm.org/citation.cfm?id=374117. 
• Alavi, Maryam; Leidner, Dorothy E. (2001). "Review: Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues". MIS Quarterly 25 (1): 107-136. http://web.njit.edu/~jerry/CIS-677/Articles/Alavi-MISQ-2001.pdf. 
• Andrus, D. Calvin (2005). "The Wiki and the Blog: Toward a Complex Adaptive Intelligence Community". Studies in Intelligence 49 (3). http://ssrn.com/abstract=755904. 
• Bontis, Nick; Choo, Chun Wei (2002). The Strategic Management of Intellectual Capital and Organizational Knowledge. New York:Oxford University Press. Template:Citation/identifier. http://choo.fis.toronto.edu/OUP/. 
• Bontis, Nick; Serenko, Alexander; Biktimirov, Ernest (2006). "MBA knowledge management course: Is there an impact after graduation?". International Journal of Knowledge and Learning 2 (3/4): 216-237. http://foba.lakeheadu.ca/serenko/papers/Bontis_Serenko_Biktimirov.pdf. 
• Bontis, Nick; Hardie, Tim; Serenko, Alexander (2008). "Self-efficacy and KM course weighting selection: Can students optimize their grades?". International Journal of Teaching and Case Studies 1 (3): 189-199. http://foba.lakeheadu.ca/serenko/papers/IJTCS_PUBLISHED.pdf. 
• Bontis, Nick; Serenko, Alexander (2009). "A follow-up ranking of academic journals". Journal of Knowledge Management 13 (1): 16-26. http://foba.lakeheadu.ca/serenko/papers/KM_Journal_Ranking_Bontis_Serenko.pdf. 
• Booker, Lorne; Bontis, Nick; Serenko, Alexander (2008). "The relevance of knowledge management and intellectual capital research". Knowledge and Process Management 15 (4): 235-246. http://foba.lakeheadu.ca/serenko/papers/Booker_Bontis_Serenko_KM_relevance.pdf. 
• Capozzi, Marla M. (2007). "Knowledge Management Architectures Beyond Technology". First Monday 12 (6). http://firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/fm/article/view/1871/1754. 
• Davenport, Tom (2008). "Enterprise 2.0: The New, New Knowledge Management?". Harvard Business Online, Feb. 19, 2008. http://discussionleader.hbsp.com/davenport/2008/02/enterprise_20_the_new_new_know_1.html. 
• Lakhani, Andrew P.; McAfee (2007). "Case study on deleting "Enterprise 2.0" article". Courseware #9-607-712, Harvard Business School. http://courseware.hbs.edu/public/cases/wikipedia/. 
• McAdam, Rodney; McCreedy, Sandra (2000). "A Critique Of Knowledge Management: Using A Social Constructionist Model". New Technology, Work and Employment 15 (2). http://papers.ssrn.com/sol3/papers.cfm?abstract_id=239247. 
• McAfee, Andrew P. (2006). "Enterprise 2.0: The Dawn of Emergent Collaboration". Sloan Management Review 47 (3): 21-28. http://sloanreview.mit.edu/the-magazine/articles/2006/spring/47306/enterprise-the-dawn-of-emergent-collaboration/. 
• McInerney, Claire (2002). "Knowledge Management and the Dynamic Nature of Knowledge". Journal of the American Society for Information Science and Technology 53 (12): 1009–1018. http://www.scils.rutgers.edu/~clairemc/KM_dynamic_nature.pdf. 
• Morey, Daryl; Maybury, Mark; Thuraisingham, Bhavani (2002). Knowledge Management: Classic and Contemporary Works. Cambridge: MIT Press. pp. 451. http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=8987. 
• Nanjappa, Aloka; Grant, Michael M. (2003). "Constructing on constructivism: The role of technology". Electronic Journal for the Integration of Technology in Education 2 (1). http://ejite.isu.edu/Volume2No1/nanjappa.pdf. 
• Nonaka, Ikujiro (1991). "The knowledge creating company". Harvard Business Review 69 (6 Nov-Dec): 96-104. http://hbr.harvardbusiness.org/2007/07/the-knowledge-creating-company/es. 
• Nonaka, Ikujiro; Takeuchi, Hirotaka (1995). The knowledge creating company: how Japanese companies create the dynamics of innovation. New York: Oxford University Press. pp. 284. http://books.google.com/books?id=B-qxrPaU1-MC. 
• Sensky, Tom (2002). "Knowledge Management". Advances in Psychiatric Treatment 8 (5): 387-395. http://apt.rcpsych.org/cgi/content/full/8/5/387. 
• Snowden, Dave (2002). "Complex Acts of Knowing - Paradox and Descriptive Self Awareness". Journal of Knowledge Management, Special Issue 6 (2): 100 - 111. Template:Citation/identifier. http://www.cognitive-edge.com/articledetails.php?articleid=13. 
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• Wenger, Etienne; McDermott, Richard; Synder, Richard (2002). Cultivating Communities of Practice: A Guide to Managing Knowledge - Seven Principles for Cultivating Communities of Practice. Boston: Harvard Business School Press. pp. 107-136. Template:Citation/identifier. http://hbswk.hbs.edu/archive/2855.html. 
• Wilson, T.D. (2002). "The nonsense of 'knowledge management'". Information Research 8 (1). http://informationr.net/ir/8-1/paper144.html. 
• Wright, Kirby (2005). "Personal knowledge management: supporting individual knowledge worker performance". Knowledge Management Research and Practice 3 (3): 156–165. Template:Citation/identifier. 

External links

• Knowledge management at the Open Directory Project