in turn means that the individuals need to put their tacit knowledge into a metaphor or analogy. Each individual could thus present his or her role, actions and approaches in terms of symbols and prototypical examples. For example, the facilitator could be a teacher, a travel guide and a midwife, all of which will make different understandings and connotations available as well as make differences in the understanding of the facilitator visible.
The essential message from this first generation of knowledge communication is that tacit knowledge can be made explicit, and for this dissertation in particular, it is essential that a way to do this is through metaphors and analogies.
From having read chapter 3 above, it is noticeable that the idea of metaphors presented by Nonaka is a CMT perspective. This comes as no surprise given that his first version of the knowledge creation company was published in 1994. The approach to metaphors is more mechanistic, and the idea of knowledge being distributed is not present in the sense put forward by e.g. Hutchins (1995). Without specifically referring to Nonaka et al., Cornelissen (2005) adds to the use of metaphors in an organizational context by stating that metaphors are “the generation and creation of new meaning beyond a previously existing similarity” rather than attributing symbols of pre-exiting attributes (ibid. p. 751). To Nonaka, knowledge resides in the individual and can be made common by metaphors as a tool. What scholars like Hutchins and Cornelissen suggest is that knowledge is distributed and that metaphors are a creation of a new common meaning rather than made from pre-existing knowledge. In the book chapter presented in chapter 10, metaphors as a management tool are presented much more thoroughly.
In the capacity of being a theory of knowledge communication, the fundamental issue with the knowledge creation model is the fact that it is hermeneutic and thus a never-ending and unspecific method of how knowledge is shared. In some senses, this critique is met by the article by Nonaka and von Krogh (2009) softening the notion of tacit and explicit knowledge into a continuum rather than a dichotomy, but they too hold on to the idea that knowledge creation is on this continuum. This position is being challenged by a Western theory from Davenport and Prusak and others.
Table 4.1: Synoptical presentation of knowledge hierarchies
Ackoff Davenport and
Prusak
Zeleny
Wisdom Evaluated
understanding
Know why Understanding Ability to apply
Knowledge Application of data and information
Comparison Consequence Connection Conversation
Know how
Information Processed data Message Know what
Data Symbols Objective facts Know nothing
by the above: thus data, information, knowledge and understanding are contained in the concept of wisdom.
The data level consists of symbols i.e. signs with no iconic or indexical link to the symbol-ized. It could be text, numbers etc. Zeleny refers to this level as “Know nothing”
Information is one step up from data and is data put into a context which gives meaning to the raw data. Data is being processed, and understanding is being added, and this will result in “Know what”.
The knowledge level is know-how and the ability of putting information into action. But Ackoff’s knowledge level does not necessarily contain the ability to shift knowledge from one context to the next. Bellinger, Castro, Mills (2004) give the example of a student being able to apply a method to the case he was shown but unable to use it in a different or more complex task.
The level of understanding relates to knowledge as learning does to memorizing. The level of understanding provides the knower with the ability to repeat a method and to synthesize old and new knowledge into new forms and structures.
The top level is wisdom and makes knowledge personal. Joint with moral, ethics and experience, wisdom is pointing forward towards generating value and evaluating the “Know why” level on Zeleny’s scale.
Rowly (2007) as well as Bellinger, Castro, Mills (2004) argue that the level of understanding should not be a separate level but rather be a transition between the levels, making the model look like the model in figure 4.2.
The model has been used in a number of different ways and has had a number of different shapes (see Rowley 2007 for an overview). The model might appear rather linear and deterministic but should be understood in the sense that the level on the scale should 42
High
High Low
Low Human Agency
Structure
DATA
INFORMATION
KNOWLEDGE
WISDOM
Underst
anding relations
Underst
anding patterns Underst
anding principles
Figure 4.2: The knowledge hierarchy from data to wisdom and including the connecting understand-ings
correspond with the level of knowledge necessary for the company in order to deliver its key product.
Using the case example of knowledge sharing between facilitators, the data from the practice should — in this understanding — be processed into information and further into knowledge by extracting patterns. If the individual can communicate the underlying principles of the practice, his or her wisdom can be communicated and thus be available to others. The level of knowledge communicated is the level of knowledge achieved.6
In sum, the second generation of knowledge communication focuses on understanding, communication and application of levels of knowledge. In the first generation, creation of knowledge through conversion was essential. In this second generation, communication of levels and qualification of knowledge is of the essence. Compared to the first generation of knowledge communication, there still is a dichotomy between low and high understanding and meaning, but the process of going from data to wisdom is engined by structure and human agency. The more processed knowledge is, the higher the understanding. This in turn is a mechanistic view of knowledge but in a different way than was seen with Nonaka above. Nonaka would state that knowledge can converge from tacit to explicit by way of metaphors. This is a very mechanical view of metaphors and lets knowledge reside in the
6It is worth noticing that the pattern in the DIKW hierarchy has an equivalent in the SOLO taxonomy (Biggs, 2011) going from unistructural to multistructural, relational and extended abstract. It is beyond the scope of this dissertation to further elaborate on this similarity and its influence on educational thinking, but it is worth mentioning that the structure is present in this domain as well. See also chapter 17.1.2.
individual. In the case of DIKW hierarchies, knowledge is a product which can be processed and refined but which still resides in the individual. This in turn conflates the 1st and 2nd generation of knowledge communication models into the dichotomy presented by Lyotard:
Either knowledge is personal, or it is a product. The difference between the two generations was investigated by Andriessen and will be explored further in chapter 5.1. This leads to the third generation of knowledge communication.