Everybody talks knowledge sharing in corporations. I never really understood what knowledge management (KM) departments were up to. So I decided to take a course in knowledge management to learn more from both a theoretical and a practical perspective. One result was the below assignment which I modified for this blog post. It summarises my thoughts about the tacit/explicit knowledge debate and about how technology can foster knowledge and information exchange in the workplace.
The main points
- By arguing that tacit knowledge is hard to write down but most valuable, KM departments are keen to extract knowledge from individuals in an organization. To do this, a lot of tools, methods and standards force them to literally write down all they know. I argue, that this is not the best way to share knowledge. Technological support based on this tacit/explicit dichotomy does not make knowledge sharing more effective. It only reinforces the idea of writing down knowledge in a digital environment.
- From my perspective, knowledge is socially-constructed. Rather than beeing either tacit or explicit, knowledge becomes information as soon as it is seperated from the original knowledge-holder and the context. While this definition holds true for others in the field, I believe that the implications for KM technology support are often overlooked.
- IT support for knowledge exchange should focus on connecting knowledgable people, enrich information by context and offer various formats for sharing information. Concrete examples could be suggestions for colleagues who have worked on similar projects, case studies (instead of “lessons learned”) and a variety of formats such as video, blog posts or forums.
Tacit vs explicit knowledge?
The difficulty with the tacit vs explicit knowledge dichotomy is that it does not highlight the relevant aspects of knowledge which are crucial in an organizational context. According to this dichotomy, tacit knowledge is difficult to write down or extract. Yet, the crucial difference does not exist between tacit and explicit knowledge but rather between knowledge and information. Explicit knowledge becomes information, tacit knowledge becomes knowledge. Without comprehending the context in which knowledge has been generated in, a receiver of the information will not be able to enhance her knowledge. Instead of placing extensive efforts on how to extract every single bit and bite of knowledge, the tasks of technology in knowledge management (KM) should be to create social connections between people and build on existing sources of information in an organization to link knowledge supply and demand. This text outlines, how moving beyond tacit-explicit-knowledge can shape more precise requirements for IT tools in knowledge management.
Some theoretical foundations
Data, information and knowledge are interwoven concepts and the foundation for an comprehensive examination of tacit and explicit knowledge. Data represents real-world phenomena whereas nformation refers to real-world phenomenon (Sundgren et al., 2003). According to Stenmark (2001), data is decontextualized information which has become too distant from the knowledge to interpret it. This statement visualizes the interconnection of the three concepts data, information and knowledge as well as the importance of context. It highlights contextual knowledge as prerequisite to interpret data. Data becomes information and finally knowledge, if both, the individual and the contextual prerequisites are given. For Stenmark, there is no value attached to the three concepts data, information and knowledge. None of the three is more valuable than the other. They are neither aligned hierarchically, nor sequenced in a determined way. Data can become information, and information can become knowledge. Also, knowledge can become information, which itself can result in data. Sundgren et al. (2003) emphasize the importance of communication and various errors in the process of communicating. Communication is the process of sharing data, not information, as the interpretation of data depends on the frameworks for interpretation. Knowledge is a mental construct consisting of information which has been generated by the individual interpretation (Sundgren et al., 2003). Collison and Parcell (2009) distinguish between tacit and explicit knowledge by pointing out that explicit knowledge is easily extracted while tacit knowledge resides in an individual’s head and is difficult to write down. As an example the authors refer to the difficulty of writing down how to write a bike. Stenmark (2001) claims that all knowledge is tacit and explicit knowledge is in fact information. Also, he identifies knowledge as the sum of information and the familiarity with concepts and context. Information does thus not hold any knowledge but requires both knowledge to be created and to be understood. Stenmark also refers to the bike example by Collison and Parcell by stating that “(…) we may not be able to fully describe (…) what happens when we ride a bike from a scientific perspective, (but) the little information provided might still be helpful. (…) Although the narrative in itself is not enough for the other part to gain a complete understanding, there are various means to describe and express feeling and actions.” (ibid, p. 7). Teams play an important role in knowledge creation. With the rise of information and communication technology, more and more teams become virtual teams. In this context, Griffith, Sawyer and Neale (2003) describe knowledge as existing on the individual and the social level. With respect to tacit and explicit knowledge, the authors state that “Hard distinctions between tacit and explicit knowledge are more often a convenience than a theoretical requirement. (…) Thus, we consider these forms of individual knowing as ranges along a continuum, although they are easier to discuss as discrete points.” (ibid, p. 270).
Sharing socially constructed knowledge
The underlying assumption is that truth, reality and knowledge are socially constructed, context-dependent and constantly re-negotiated. Communication as a key factor in transferring information depicts that IT in knowledge management should support communication rather than extracting and storing information. The dichotomy between tacit and explicit knowledge implies, that effort must be dedicated to extract all knowledge to information, make it readable, visible or hearable. This would mean, that the sender and her context need to be known at all times. In organizations, there can be many receivers for a certain type of information and thus also various ways of displaying it. Here, Stenmark’s definition comes in handy. There is no such thing as explicit knowledge, rather explicit knowledge is information. Knowledge can be transferred by different formats of information across (virtual) teams. For knowledge management to be effective, it could be beneficial to focus less on extracting and updating information and focus more on connecting employees and the various information artifacts they produce every day. This gives a new direction for IT systems in KM. One way of displaying information might be not enough to enhance an individual’s knowledge, that is why IT systems should scaffold diverse ways of transmitting information. For different purposes, individuals and contexts, there must be different means available to share knowledge. These could be for example in written form, but also in other formats such as videos, podcasts, pictures, etc. Also, IT systems for KM purposes should facilitate the exchange of contextual data, such as the department an employee works for, contact data and the like which could be used to identify an individual’s expertise and to improve contextualising the information an individual shares. In addition, to create a common framework how knowledge should be shared (such as in written form as “lessons learned”) might help in a limited context but needs to be adjusted, changed, re-created and re-combined later on. Imagine engineers sharing information about a project and their lessons learned. An employee form the HR department might be interested in the quality of team work and the contribution of each team member. However, the KM framework and the context applied for projects in engineering would not necessarily answer these questions. Thus, an option for the HR employee is to contact one of the project members to ask specific questions. Based on this, new information can be created. Instead of assuming what a possible reader of information might want to know, IT systems in KM should focus on establishing connections between employees who can supply the information in question. Furthermore, the updating process in KM is time-consuming. Possibly, this updating process could be bypassed by letting go of a mandatory update and versioning standards. Employees create and re-create their information artifacts based on recent needs and based on connections between each other in real life.
Social information networks where the nodes are actual people, not standardized documents
The question is not if IT can manage all kinds of knowledge but rather if IT support for KM is able to facilitate the establishment of connection between employees. The bottleneck of the information flow in organizations is externalizing knowledge to information, transferring it to the right receiver and updating it constantly. In future, it will not be possible to standardize how to externalize knowledge and to standardize how and when to update it due to shorter time cycles and the constant need for real-time information artifacts. It will be important that KM processes and efforts foster the connection of people and that they can recreate information artifacts based on what already exists. A KM IT system should establish connections between people not between information, display additional contextual information about the users as well as offer different formats for sharing information (and integrate them seemlessly). IT systems to support KM should be built on already existing information about employees including their generated information artifacts to analyze potential beneficial connections between employees and foster the networking between different functions.
- Collison, C., & Parcell, G. (2009). Learning to fly practical knowledge management from some of the world’s leading learning organizations. Chichester, West Sussex: Capstone.
- Griffith, Terri L., Sawyer, John E., & Neale, Margaret A. (2003). Virtualness and knowledge in teams: Managing the love triangle of organizations, individuals, and information technology (1). (Special Issue). MIS Quarterly, 27(2), 265.
- Stenmark, D. (2001). The Relationship between Information and Knowledge, in Proceedings of IRIS 24, Ulvik, Norway, August 11-14. Sundgren, B, Martensson, P.,
- Mähring, M. & Nilsson, K. (Eds.) (2003). Exploring patterns in information management: Concepts and perspectives for understanding IT-related change, Economic Research Institute, Stockholm School of Economics, Stockholm.
- Walsham, G. (2001). Knowledge Management: The Benefits and Limitations of Computer Systems. European Management Journal, 19(6), 599-608.