Information Technology Reference
In-Depth Information
autopoietic view of knowledge, in which the brain constructs its own meaning from
information as opposed to the traditional view which sees knowledge as “universal
and objective and which can be used interchangeably with the terms data and infor-
mation” (Vicari, Krogh, Roos, & Mahnke, 1996). This has major implications for
the field of IT-based KMS. First, KMS should be systems that support knowledge
sharing (not systems to generate knowledge). Second, explicit knowledge is infor-
mation that needs to be interpreted. Thus, knowledge is gained by interpreting and
interacting with information (Alavi & Leidner, 2001).
The use of DOE allows one to gather the maximum amount of knowledge while
using the minimum amount of resources, the application of the technique itself pro-
vides a road map to improvement and the possibility of avoiding large amounts of
capital expenditure as a result of a successful experiment (Goh, 2002).
DOE as with any form of experimentation is in itself a knowledge creation pro-
cess. It is a method that allows an engineer to understand the product better in
terms of factors that can influence its specification. This knowledge can be made
explicit using a number of tools from the outputs of the analysis of the experimen-
tal data. Prior research suggests that the use of quality tools and methods can lead
to knowledge creation (Choo, Linderman, & Schroeder, 2002. Structured method
and motivational potential in knowledge creation: linking quality and knowledge.
University of Minnesota Working Paper.).
Knowledge creation involves a number of phases consisting of tacit and explicit
knowledge. Tacit and explicit knowledge are mutually dependant on each other,
i.e., to understand explicit knowledge tacit knowledge is required (Alavi and
Leidner, 2001). For example, to be able to read a document you need an under-
standing of the meaning of the symbols (words and numbers) on the page
(Beijerse, 2000).
The knowledge creation process as outlined by Nonaka et al. (2000) is a spiral,
consisting of four phases - externalization, combination, internalization, and social-
ization. It consists of a conversion process between tacit and explicit knowledge.
As the creation process spirals through the interaction between tacit and explicit
knowledge the amount of knowledge in the organization expands.
This process applies also equally well to the DOE context too - the design and
execution of experiments increase an organization's knowledge of product devel-
opment processes, resulting in new as well as improved products and processes.
In applying Nonaka's spiral of knowledge creation, we can conclude that the DOE
approach creates new knowledge as follows:
Externalization (tacit to explicit) - documenting in some way the results or
findings of the experiment.
Combination (explicit to explicit) - selecting multiple sources of explicit knowl-
edge and combining it into some form which the individual understands.
Internalization (explicit to tacit) - using existing information to conduct addi-
tional experiments and further their knowledge.
Socialization (tacit to tacit) - sharing what you have learned with other team
members.
Search WWH ::




Custom Search