Knowledge management

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Short description: Process of creating, sharing, using and managing the knowledge and information of an organization


Knowledge management (KM) is the collection of methods relating to creating, sharing, using and managing the knowledge and information of an organization.[1] It refers to a multidisciplinary approach to achieve organisational objectives by making the best use of knowledge.[2]

An established discipline since 1991,[3] KM includes courses taught in the fields of business administration, information systems, management, library, and information science.[3][4] Other fields may contribute to KM research, including information and media, computer science, public health and public policy.[5] Several universities offer dedicated master's degrees in knowledge management.

Many large companies, public institutions, and non-profit organisations have resources dedicated to internal KM efforts, often as a part of their business strategy, IT, or human resource management departments.[6] Several consulting companies provide advice regarding KM to these organizations.[6]

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.[7] These 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 on encouraging the sharing of knowledge.[2][8] KM is an enabler of organizational learning.[9][10]

The most complex scenario for knowledge management may be found in the context of supply chain as it involves multiple companies without an ownership relationship or hierarchy between them, being called by some authors as transorganizational or interorganizational knowledge. That complexity is additionally increased by industry 4.0 (or 4th industrial revolution) and digital transformation, as new challenges emerge from both the volume and speed of information flows and knowledge generation.[11]

History

Knowledge management efforts have a long history, including on-the-job discussions, formal apprenticeship, discussion forums, corporate libraries, professional training, and mentoring programs.[2][10] With increased use of computers in the second half of the 20th century, specific adaptations of technologies such as knowledge bases, expert systems, information repositories, group decision support systems, intranets, and computer-supported cooperative work have been introduced to further enhance such efforts.[2]

In 1999, the term personal knowledge management was introduced; it refers to the management of knowledge at the individual level.[12]

In the enterprise, early collections of case studies recognised the importance of knowledge management dimensions of strategy, process and measurement.[13][14] 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.[14] In short, knowledge management programs can yield impressive benefits to individuals and organisations if they are purposeful, concrete and action-orientated.

Research

KM emerged as a scientific discipline in the early 1990s.[15] It was initially supported by individual practitioners, when Skandia hired Leif Edvinsson of Sweden as the world's first Chief Knowledge Officer (CKO).[16] Hubert Saint-Onge (formerly of CIBC, Canada), started investigating KM long before that.[2] The objective of CKOs is to manage and maximise the intangible assets of their organizations.[2] Gradually, CKOs became interested in practical and theoretical aspects of KM, and the new research field was formed.[17] 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).[3][18]

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 organizations.[19] The KM discipline has been gradually moving towards academic maturity.[2] First, is a trend toward higher cooperation among academics; single-author publications are less common. Second, the role of practitioners has changed.[17] Their contribution to academic research declined from 30% of overall contributions up to 2002, to only 10% by 2009.[20] Third, the number of academic knowledge management journals has been steadily growing, currently reaching 27 outlets.[21][22]

Multiple KM disciplines exist; approaches vary by author and school.[17][23] As the discipline matured, academic debates increased regarding theory and practice, including:

  • Techno-centric with a focus on technology, ideally those that enhance knowledge sharing and creation.[24][25]
  • Organisational with a focus on how an organisation can be designed to facilitate knowledge processes best.[6]
  • Ecological with a focus on the interaction of people, identity, knowledge, and environmental factors as a complex adaptive system akin to a natural ecosystem.[26][27]

Regardless of the school of thought, core components of KM roughly include people/culture, processes/structure and technology. The details depend on the perspective.[28] KM perspectives include:

The practical relevance of academic research in KM has been questioned[35] with action research suggested as having more relevance[36] and the need to translate the findings presented in academic journals to a practice.[13]

Dimensions

Different frameworks for distinguishing between different 'types of' knowledge exist.[10] One proposed framework for categorising the dimensions of knowledge distinguishes tacit knowledge and explicit knowledge.[32] Tacit knowledge represents internalised knowledge that an individual may not be consciously aware of, such as to accomplish 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.[17][37]

The Knowledge Spiral as described by Nonaka & Takeuchi.

Ikujiro Nonaka proposed a model (SECI, for Socialisation, Externalisation, Combination, Internalisation) which considers a spiraling interaction between explicit knowledge and tacit knowledge.[38] In this model, knowledge follows a cycle in which implicit knowledge is 'extracted' to become explicit knowledge, and explicit knowledge is 're-internalised' into implicit knowledge.[38]

Hayes and Walsham (2003) describe knowledge and knowledge management as two different perspectives.[39] The content perspective suggests that knowledge is easily stored; because it may be codified, while the relational perspective recognises the contextual and relational aspects of knowledge which can make knowledge difficult to share outside the specific context in which it is developed.[39]

Early research suggested that KM needs to convert internalised tacit knowledge into explicit knowledge to share it, and the same effort must permit individuals to internalise and make personally meaningful any codified knowledge retrieved from the KM effort.[6][40]

Subsequent research suggested that a distinction between tacit knowledge and explicit knowledge represented an oversimplification and that the notion of explicit knowledge is self-contradictory.[12] Specifically, for knowledge to be made explicit, it must be translated into information (i.e., symbols outside our heads).[12][41] More recently, together with Georg von Krogh and Sven Voelpel, Nonaka returned to his earlier work in an attempt to move the debate about knowledge conversion forward.[4][42]

A second proposed framework for categorising knowledge dimensions distinguishes embedded knowledge of a system outside a human individual (e.g., an information system may have knowledge embedded into its design) from embodied knowledge representing a learned capability of a human body's nervous and endocrine systems.[43]

A third proposed framework 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.[39][44] Collaborative environments such as communities of practice or the use of social computing tools can be used for both knowledge creation and transfer.[44]

Strategies

Knowledge may be accessed at three stages: before, during, or after KM-related activities.[31] Organisations have tried knowledge capture incentives, including making content submission mandatory and incorporating rewards into performance measurement plans.[45] Considerable controversy exists over whether such incentives work and no consensus has emerged.[7]

One strategy to KM involves actively managing knowledge (push strategy).[7][46] 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 (codification).[46] Another strategy involves individuals making knowledge requests of experts associated with a particular subject on an ad hoc basis (pull strategy).[7][46] In such an instance, expert individual(s) provide insights to requestor (personalisation).[32] When talking about strategic knowledge management, the form of the knowledge and activities to share it defines the concept between codification and personalization. [47] The form of the knowledge means that it’s either tacit or explicit. Data and information can be considered as explicit and know-how can be considered as tacit. [48]

Hansen et al. defined the two strategies (codification and personalisation).[49] Codification means a system-oriented method in KM strategy for managing explicit knowledge with organizational objectives.[50] Codification strategy is document-centered strategy, where knowledge is mainly codified as “people-to-document” method. Codification relies on information infrastructure, where explicit knowledge is carefully codified and stored.[49] Codification focuses on collecting and storing codified knowledge in electronic databases to make it accessible.[51] Codification can therefore refer to both tacit and explicit knowledge.[52] In contrast, personalisation encourages individuals to share their knowledge directly.[51] Personification means human-oriented KM strategy where the target is to improve knowledge flows through networking and integrations related to tacit knowledge with knowledge sharing and creation.[53] Information technology plays a less important role, as it only facilitates communication and knowledge sharing.

Other knowledge management strategies and instruments for companies include:[7][26][32]

  • Knowledge sharing (fostering a culture that encourages the sharing of information, based on the concept that knowledge is not irrevocable and should be shared and updated to remain relevant)
    • Make knowledge-sharing a key role in employees' job description
    • Inter-project knowledge transfer
    • Intra-organisational knowledge sharing
    • Inter-organisational knowledge sharing
    • Knowledge retention also known as Knowledge Continuation: activities addressing the challenge of knowledge loss as a result of people leaving[54][55][56]
    • Mapping knowledge competencies, roles and identifying current or future predicted gaps.
    • Defining for each chosen role the main knowledge that should be retained, and building rituals in which the knowledge is documented or transferred on, from the day they start their job.
    • Transfer of knowledge and information prior to employee departure by means of sharing documents, shadowing, mentoring, and more,
  • Proximity & architecture (the physical situation of employees can be either conducive or obstructive to knowledge sharing)
  • Storytelling (as a means of transferring tacit knowledge)
  • Cross-project learning
  • After-action reviews
  • Knowledge mapping requires the organization to know what kind of knowledge organization has and how is it distributed throughout the company, and how to efficiently use and re-use that knowledge. (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)
  • Expert systems (knowledge seeker responds to one or more specific questions to reach knowledge in a repository)
  • Best practice transfer
  • Knowledge fairs
  • Competency-based management (systematic evaluation and planning of knowledge related competences of individual organisation members)
  • Master–apprentice relationship, Mentor-mentee relationship, job shadowing
  • Collaborative software technologies (wikis, shared bookmarking, blogs, social software, 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 a specific subject)
  • Knowledge farming (using note-taking software to cultivate a knowledge graph, part of knowledge agriculture)
  • Knowledge capturing (refers to a process where trained people extract valuable or else desired knowledge from experts and embed it in databases)

Motivations

Multiple motivations lead organisations to undertake KM.[37] Typical considerations include:[32][57]

  • Making available increased knowledge content in the development and provision of products and services
  • Achieving shorter development cycles
  • Improving consistency of knowledge and standardized expert skills among staff
  • Facilitating and managing innovation and organisational learning
  • Leveraging expertises 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 assets in the workforce (such as the expertise and know-how possessed by key individuals or stored in repositories)

KM technologies

Knowledge management (KM) technology can be categorised:

  • Collaborative software(Groupware)—Software that facilitates collaboration and sharing of organisational information. Such applications provide tools for threaded discussions, document sharing, organisation-wide uniform email, and other collaboration-related features.
  • Workflow systems—Systems that allow the representation of processes associated with the creation, use and maintenance of organisational knowledge, such as the process to create and utilise forms and documents.
  • Content management and document management systems—Software systems that automate the process of creating web content and/or documents. Roles such as editors, graphic designers, writers and producers can be explicitly modeled along with the tasks in the process and validation criteria. Commercial vendors started either to support documents or to support web content but as the Internet grew these functions merged and vendors now perform both functions.
  • Enterprise portals—Software that aggregates information across the entire organisation or for groups such as project teams.
  • eLearning—Software that enables organisations to create customised training and education. This can include lesson plans, monitoring progress and online classes.
  • Planning and scheduling software—Software that automates schedule creation and maintenance. The planning aspect can integrate with project management software.[24]
  • Telepresence—Software that enables individuals to have virtual "face-to-face" meetings without assembling at one location. Videoconferencing is the most obvious example.
  • Semantic technology such as ontologies—Systems that encode meaning alongside data to give machines the ability to extract and infer information.[58]

These categories overlap. Workflow, for example, is a significant aspect of a content or document management systems, most of which have tools for developing enterprise portals.[7][59]

Proprietary KM technology products such as Lotus Notes defined proprietary formats for email, documents, forms, etc. The Internet drove most vendors to adopt Internet formats. Open-source and freeware tools for the creation of blogs and wikis now enable capabilities that used to require expensive commercial tools.[36][60]

KM is driving the adoption of tools that enable organisations to work at the semantic level,[61] as part of the Semantic Web.[62] Some commentators have argued that after many years the Semantic Web has failed to see widespread adoption,[63][64][65] while other commentators have argued that it has been a success.[66]

Knowledge barriers

Just like knowledge transfer and knowledge sharing, the term "knowledge barriers" is not a uniformly defined term and differs in its meaning depending on the author.[67] Knowledge barriers can be associated with high costs for both companies and individuals.[68][69][70]

Knowledge retention

Knowledge retention is part of knowledge management. It helps convert tacit form of knowledge into an explicit form. It is a complex process which aims to reduce the knowledge loss in the organization. [71] Knowledge retention is needed when expert knowledge workers leave the organization after a long career. [72] Retaining knowledge prevents losing intellectual capital. [73]

According to DeLong(2004) [74] knowledge retention strategies are divided into four main categories:

  • Human resources, processes and practices
  • Knowledge transfer practices
  • Knowledge recovery practices
  • Information technologies used to capture, store and share knowledge.

Knowledge retention projects are usually introduced in three stages: decision making, planning and implementation. There are differences among researchers on the terms of the stages. For example, Dalkir talks about knowledge capture, sharing and acquisition and Doan et al. introduces initiation, implementation and evaluation. [75][76] Furthermore, Levy introduces three steps (scope, transfer, integration) but also recognizes a “zero stage” for initiation of the project.[72]

Knowledge Management Cycle

1. The Zack KM Cycle

"Models of KM Cycle" (n.d.) stated that work on information product design and development provides the basis for the Zack model. This network that connects each stage is designed to be logical and consistent in Meyer and Zack's method. The major stages of a knowledge repository's development are mapped to the stages of a KM cycle in this cycle. Acquisition, refinement, distribution, storage/retrieval, and presentation/use are the stages. The "refinery" is another name for this cycle. The scope, breadth, depth, credibility, accuracy, timeliness, relevance, cost, control, and exclusivity of raw materials are all aspects of the acquisition. Moving from one medium to another or restructuring, relabeling, indexing, and integrating are examples of refinement. The upstream addition and refinement stages that feed the repository and the downstream product generation stages are connected by storage or retrieval. The term "distribution" encompasses not only how the product will be delivered to the end user (such as fax, print, or email) but also the timing, frequency, form, language, and other aspects of that delivery. During the stage of application or presentation, context is very important. One of the most comprehensive representations of the key components of the knowledge management model is the Meyer and Zack model. To be more specific, refinement is a crucial stage in the KM cycle that is frequently overlooked.

2. The Bukowitz and Williams KM Cycle "Models of KM Cycle" (n.d.) stated that the knowledge management process framework developed by Bukowitz and Williams describes "how organizations generate, maintain, and expand a strategically correct stock of knowledge to create value." These stages focus on matching intellectual capital to strategic needs over a long period.

  • The Get Stage is the first step, and it involves finding the information needed to make decisions, solve problems, or come up with new ideas.
  • The Use Stage is the next and focuses on new and different ways to combine information to encourage organizational innovation. Individuals are the main focus, followed by groups.
  • As a strategy for achieving competitive advantage, the Learn Stage emphasizes the formal process of learning from experiences. Learning is important in businesses because it serves as a transition point between putting ideas into practice and coming up with new ones.
  • In the Knowledge Management cycle's Contribute Stage, employees are encouraged to post what they have learned to the communal knowledge base (similar to a repository). Individual knowledge can only be made visible and accessible to the entire organization in these circumstances.


3. The McElroy KM Cycle

"Models of KM Cycle" (n.d.) stated that McElroy outlines a knowledge life cycle that includes the processes of knowledge production and knowledge integration, as well as a series of feedback loops to the business-processing environment, organizational memory, and beliefs and claims. The first step in organizational learning is individual and group learning. Codification at the organizational level is a component of knowledge claim validation. For the receipt and codification of individual and group innovations, a formal procedure is necessary. The process by which an organization acquires knowledge claims or information produced by others, typically outside the company, whether intentionally or unintentionally is known as information addition. At the organizational level, the formulation of new knowledge claims is fundamentally influenced by this stage. The process by which an organization announces new knowledge claims to its operating environment and retires old ones is known as knowledge integration. It encompasses all forms of knowledge transfer, including teaching, knowledge sharing, and other social activities that either link knowledge workers to previously acquired knowledge or accommodate newly acquired knowledge.


4. The Wiig KM Cycle

"Models of KM Cycle" (n.d.) stated that the Wiig KM cycle looks at how people and organizations build and use knowledge. Because it addresses the organization as a whole and includes business areas that are typically found in the majority of organizations, the model is highly favored in KM. He proposes that the way knowledge is created, utilized in problem-solving and decision-making, and manifested cognitively in addition to culture, technology, and procedures is the foundation of KM. Wiig focuses on the following three requirements for a company to be successful in its operations: It must have the ability to act, have resources (people, capital, and facilities), and have a business (products or services) and customers. The Wiig KM cycle emphasizes the third point.

5. An Integrated KM Cycle

When implemented in any organization, the integrated cycles of knowledge management strategy consist of the following three major stages:

  • Knowledge capture and/or creation

The term "Knowledge Capture" refers to the process of regularly identifying and codifying existing internal (mostly unnoticed) organizational knowledge and expertise as well as external (environmental) knowledge.

  • Knowledge sharing and dissemination

The advancement of novel technologies and knowledge innovations that did not previously exist within the organization is known as knowledge creation.

The next step is to contextualize the newly identified content once it is evident that it has sufficient value. Maintaining a connection between those knowledgeable about the content and the knowledge is necessary for this.

  • Knowledge acquisition and application

"Models of KM Cycle" (n.d.) stated that contextualization refers to figuring out the most important parts of the content to make it more suitable for a wide range of users. Finally, contextualization is successful when the new content is securely, precisely, and seamlessly integrated into the company's business procedures.


Reference:

Tutorials Point. (2023). Models of Km Cycle https://www.tutorialspoint.com/knowledge_management /models_of_km_cycle.htm

See also


References

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  46. ↑ 46.0 46.1 46.2 "Knowledge Management for Data Interoperability". http://www.cs.fiu.edu/~chens/PDF/IRI00_Rathau.pdf. 
  47. ↑ Venkitachalam & Willmott (2017)
  48. ↑ Laihonen, Harri ; Hannula, Mika; Helander, Nina; Ilvonen, Ilona; Jussila (2013)
  49. ↑ 49.0 49.1 Hansen et al., 1999
  50. ↑ "What's Your Strategy for Managing Knowledge? Morten T. Hansen, Nitin Nohria, and Thomas Tierney", The Knowledge Management Yearbook 2000–2001 (Routledge): pp. 66–80, 2013-05-13, doi:10.4324/9780080941042-9, ISBN 978-0-08-094104-2, http://dx.doi.org/10.4324/9780080941042-9, retrieved 2022-04-26 
  51. ↑ 51.0 51.1 Smith (2004), p. 7
  52. ↑ Hall (2006), pp. 119f
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  55. ↑ DeLong, D. W., & Storey, J. (2004). Lost knowledge: Confronting the threat of an aging workforce. Oxford University Press
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  57. ↑ "Managing knowledge in manufacturing". https://nsflow.com/blog/knowledge-management-in-manufacturing-why-is-it-so-important. 
  58. ↑ Davies, John; Grobelnik, Marko; Mladenić, Dunja, eds (2009). Semantic Knowledge Management: Integrating Ontology Management, Knowledge Discovery, and Human Language Technologies. Berlin: Springer-Verlag. doi:10.1007/978-3-540-88845-1. ISBN 9783540888444. OCLC 312625476. 
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  60. ↑ Calvin, D. Andrus (2005). "The Wiki and the Blog: Toward a Complex Adaptive Intelligence Community". Studies in Intelligence 49 (3). 
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  62. ↑ Berners-Lee, Tim; Hendler, James; Lassila, Ora (May 17, 2001). "The Semantic Web A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities". Scientific American 284 (5): 34–43. doi:10.1038/scientificamerican0501-34. http://www.cs.umd.edu/~golbeck/LBSC690/SemanticWeb.html. 
  63. ↑ Bakke, Sturla; ygstad, Bendik (May 2009). "Two emerging technologies: a comparative analysis of Web 2.0 and the Semantic Web". CONF-IRM 2009 Proceedings (28). http://aisel.aisnet.org/confirm2009/28. "Our research question is: how do we explain the surprising success of Web 2.0 and the equally surprising non-fulfillment of the Semantic Web. Building on a case study approach we conducted a in depth comparative analysis of the two emerging technologies. We propose two conclusions. First, traditional top-down management of an emerging global technology has proved not to be effective in the case of the Semantic Web and Web 2.0, and second, the success for such global technologies is mainly associated with bootstrapping an already installed base.". 
  64. ↑ Grimes, Seth (7 January 2014). "Semantic Web business: going nowhere slowly". InformationWeek. https://www.informationweek.com/software/information-management/semantic-web-business-going-nowhere-slowly/d/d-id/1113323. Retrieved 5 September 2017. "SemWeb is a narrowly purposed replica of a subset of the World Wide Web. It's useful for information enrichment in certain domains, via a circumscribed set of tools. However, the SemWeb offers a vanishingly small benefit to the vast majority of businesses. The vision persists but is unachievable; the business reality of SemWeb is going pretty much nowhere.". 
  65. ↑ Cagle, Kurt (3 July 2016). "Why the Semantic Web has failed". LinkedIn. https://www.linkedin.com/pulse/why-semantic-web-has-failed-kurt-cagle. "This may sound like heresy, but my personal belief is that the semantic web has failed. Not in "just give it a few more years and it'll catch on" or "it's just a matter of tooling and editors". No, I'd argue that, as admirable as the whole goal of the semantic web is, it's just not working in reality." 
  66. ↑ Zaino, Jennifer (23 September 2014). "The Semantic Web's rocking, and there ain't no stopping it now". http://www.dataversity.net/semantic-webs-rocking-aint-stopping-now/. "Make no mistake about it: The semantic web has been a success and that's not about to stop now. That was essentially the message delivered by W3C Data Activity Lead Phil Archer, during his keynote address celebrating the semantic web's ten years of achievement at last month's Semantic Technology & Business Conference in San Jose." 
  67. ↑ Paulin, Dan Theodor; Suneson, K (January 2011). "Knowledge Transfer, Knowledge Sharing and Knowledge Barriers-Three Blurry Terms in KM". https://www.researchgate.net/publication/286105137. 
  68. ↑ Dalkir, Kimiz (2005). Knowledge management in theory and practice. pp. 221, 276–289. doi:10.4324/9780080547367. ISBN 9781136389757. https://doi.org/10.4324/9780080547367. Retrieved May 1, 2022. 
  69. ↑ Riege, Andreas (June 1, 2005). "Three-dozen knowledge-sharing barriers managers must consider". Journal of Knowledge Management 9 (3): 18–35. doi:10.1108/13673270510602746. https://doi.org/10.1108/13673270510602746. Retrieved May 2, 2022. 
  70. ↑ Riege, Andreas (February 2007). "Actions to overcome knowledge transfer barriers in MNCs". Journal of Knowledge Management 11 (1): 48–67. doi:10.1108/13673270710728231. https://doi.org/10.1108/13673270710728231. Retrieved May 2, 2022. 
  71. ↑ Bolisani, Ettore; Bratianu, Constantin (2018). Generic Knowledge Strategies. https://www.researchgate.net/publication/318234758. 
  72. ↑ 72.0 72.1 Levy, Moria (2011-01-01). "Knowledge retention: minimizing organizational business loss". Journal of Knowledge Management 15 (4): 582–600. doi:10.1108/13673271111151974. ISSN 1367-3270. https://doi.org/10.1108/13673271111151974. 
  73. ↑ Urbancova, Hana (2012-06-30). "The Process of Knowledge Continuity Ensuring". Journal of Competitiveness 4 (2): 38–48. doi:10.7441/joc.2012.02.03. http://www.cjournal.cz/index.php?hid=clanek&cid=94. 
  74. ↑ Delong, DW (2004). Lost Knowledge: Confronting the threat of aging workforce.. 
  75. ↑ Dalkir, Kimiz (2013-09-05) (in en). Knowledge Management in Theory and Practice (1 ed.). Routledge. doi:10.4324/9780080547367. ISBN 978-0-08-054736-7. https://www.taylorfrancis.com/books/9780080547367. 
  76. ↑ "A Reference Model for Knowledge Retention within Small and Medium-Sized Enterprises". Proceedings of the International Conference on Knowledge Management and Information Sharing (Paris, France: SciTePress – Science and Technology Publications): 306–311. 2011. doi:10.5220/0003632003060311. ISBN 978-989-8425-81-2. http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0003632003060311. 

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