Tacit knowledge

The Definition of Tacit Knowledge

This type of knowledge was originally defined by Polanyi in 1966. It is sometimes referred to as know-how (Brown & Duguid 1998) and refers to intuitive, hard to define knowledge that is largely experience based. Because of this, tacit knowledge is often context dependent and personal in nature. It is hard to communicate and deeply rooted in action, commitment, and involvement (Nonaka 1994).

Tacit knowledge is also regarded as being the most valuable source of knowledge, and the most likely to lead to breakthroughs in the organization (Wellman 2009). Gamble & Blackwell (2001) link the lack of focus on tacit knowledge directly to the reduced capability for innovation and sustained competitiveness.

KMS have a very hard time handling this type of knowledge. An IT system relies on codification, which is something that is difficult/impossible for the tacit knowledge holder.

Using a reference by Polanyi (1966), imagine trying to write an article that would accurately convey how one reads facial expressions. It should be quite apparent that it would be near impossible to convey our intuitive understanding gathered from years of experience and practice. Virtually all practitioners rely on this type of knowledge. An IT specialist for example will troubleshoot a problem based on his experience and intuition. It would be very difficult for him to codify his knowledge into a document that could convey his know-how to a beginner. This is one reason why experience in a particular field is so highly regarded in the job market.

The exact extent to which IT systems can aid in the transfer and enhancement of tacit knowledge is a rather complicated discussion. For now, suffice it to say that successful KM initiatives must place a very strong emphasis on the tacit dimension, focusing primarily on the people involved, and they must understand the limitations imposed by computerized systems.

Tacit knowledge is found in: the minds of human stakeholders. It includes cultural beliefs, values, attitudes, mental models, etc. as well as skills, capabilities and expertise (Botha et al 2008). On this site, I will generally limit tacit knowledge to knowledge embodied in people, and refer separately to embedded knowledge (as defined below), whenever making this distinction is relevant.

Differences with explicit knowledge

Tacit knowledge can be distinguished from explicit knowledge in three major areas:

  • Codifiability and mechanism of transferring knowledge: while explicit knowledge can be codified, and easily transferred without the knowing subject, tacit knowledge is intuitive and unarticulated knowledge that cannot be communicated, understood or used without the ‘knowing subject’. Unlike the transfer of explicit knowledge, the transfer of tacit knowledge requires close interaction and the buildup of shared understanding and trust among them.
    • Main methods for the acquisition and accumulation: Explicit knowledge can be generated through logical deduction and acquired through practical experience in the relevant context. In contrast, tacit knowledge can only be acquired through practical experience in the relevant context.
      • Potential of aggregation and modes of appropriation: Explicit knowledge can be aggregated at a single location, stored in objective forms and appropriated without the participation of the knowing subject. Tacit knowledge in contrast, is personal contextual. It is distributive, and cannot easily be aggregated. The realization of its full potential requires the close involvement and cooperation of the knowing subject.

      The process of transforming tacit knowledge into explicit or specifiable knowledge is known as codification, articulation, or specification. The tacit aspects of knowledge are those that cannot be codified, but can only be transmitted via training or gained through personal experience. There is a view against the distinction, where it is believed that all propositional knowledge (knowledge that) is ultimately reducible to practical knowledge (knowledge how).

      Examples of Tacit Knowledge

      • One of the most convincing examples of tacit knowledge is facial recognition. ‘‘We know a person’s face, and can recognize it among a thousand, indeed a million. Yet we usually cannot tell how we recognize a face we know, so most of this cannot be put into words.’’ When you see a face, you are not conscious about your knowledge of the individual features (eye, nose, mouth), but you see and recognize the face as a whole.
      • Another example of tacit knowledge is the notion of language itself – it is not possible to learn a language just by being taught the rules of grammar – a native speaker picks it up at a young age, almost entirely unaware of the formal grammar which they may be taught later. Other examples are how to ride a bike, how tight to make a bandage, or knowing whether a senior surgeon feels an intern may be ready to learn the intricacies of surgery; this can only be learned through personal experimentation.
      • Collins showed that Western laboratories long had difficulties in successfully replicating an experiment (in this case, measuring the quality, Q, factors of sapphire) which the team led by Vladimir Braginsky at Moscow State University had been conducting for twenty years. Western scientists became suspicious of the Russian results and it was only when Russian and Western scientists conducted the measurements collaboratively that the trust was reestablished. Collins argues that laboratory visits enhance the possibility for the transfer of tacit knowledge.
      • Another example is the Bessemer steel process – Bessemer sold a patent for his advanced steelmaking process and was sued by the purchasers who couldn't get it to work. In the end Bessemer set up his own steel company because he knew how to do it, even though he could not convey it to his patent users. Bessemer's company became one of the largest in the world and changed the face of steel making.
      • When Matsushita started developing its automatic home bread-making machine in 1985, an early problem was how to mechanize the dough-kneading process, a process that takes a master baker years of practice to perfect. To learn this tacit knowledge, a member of the software development team, Ikuko Tanaka, decided to volunteer herself as an apprentice to the head baker of the Osaka International Hotel, who was reputed to produce the area’s best bread. After a period of imitation and practice, one day she observed that the baker was not only stretching, but also twisting the dough in a particular fashion (“twisting stretch”), which turned out to be his secret for making tasty bread. The Matsushita home bakery team drew together eleven members from completely different specializations and cultures: product planning, mechanical engineering, control systems, and software development. The “twisting stretch” motion was finally materialized in a prototype, after a year of iterative experimentation by the engineers and team members working closely together, combining their explicit knowledge. For example, the engineers added ribs to the inside of the dough case in order to hold the dough better as it is being churned. Another team member suggested a method (later patented) to add yeast at a later stage in the process, thereby preventing the yeast from over-fermenting in high temperatures.