Knowledge representation within information systems in manufacturing environments
Representing knowledge as information content alone is insufficient in providing us with an understanding of the world around us. A combination of context as well as reasoning of the information content is fundamental to representing knowledge in an information system. Knowledge Representation is typically concerned with providing structures and theories that are used as a basis for intelligent reasoning. For this research however, the author defines an alternative meaning, which is related to how knowledge is used in a given context. Thus, this dissertation provides a contribution to the field of knowledge within information systems, in terms of the development of a frame-of-reference that will support the reader in navigating through the different forms of explicit and tacit knowledge use within the manufacturing industry. In doing so, the dissertation also presents the generation of a novel classification of three forms of knowledge (Structural, Interpretive and Evaluative forms); the development of a conceptual framework which highlights the drivers for knowledge transformation; and the development of a conceptual model which seeks to envelop both the content as well as the context of knowledge (Semiotic as well as Symbiotic factors). This is established through the use of an Empirical, Quantitative case study approach, that seeks to explore an interpretivist view of knowledge representation within two information systems contexts, within two UK manufacturing organisations. The first case study presents how a-priori knowledge assumptions are used in a computer aided engineering decision-making task within a high technology manufacturing company. The second case study shows how knowledge is used within the IT/IS investment evaluation decision making process, within a manufacturing SME. In doing so, both case studies attempt to elucidate the inherent, underlying relationship between explicit and tacit knowledge, via a frame-of-reference developed by the author which defines key drivers for knowledge transformation.