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|Title:||Development of an intellectual capital driven knowledge audit methodology for managing unstructured knowledge||Authors:||Gu, Jie||Degree:||M.Phil.||Issue Date:||2013||Abstract:||The mining of important knowledge assets of an organisation is both a time consuming and often subjective process. A key task in this process is to determine which of the asset items are important to the business activities and goals of the company. This research tackles the shortcomings of the traditional knowledge audit methodology which operates on well-structured business flow and process. These traditional methodologies are not applicable for knowledge intensive companies which often have business processes that are highly unstructured and not well-defined such as works in consultancy, marketing or product development. Knowledge assets of these task-oriented companies do not merely reside in structured forms such as databases and specifications, but are also embedded in many scattered and massive unstructured sources such as emails, meeting records, publications, newsletters, websites, chat rooms, instant messages, blogs, etc. This research project aims to develop a method to elicit the knowledge asset items and assess their relative importance for subsequent knowledge capture from the vast amount of unstructured information embedded in various sources of a company. An intellectual capital (IC) framework which classifies the important intangible assets of an organisation into human capital (HC), structural capital (SC) and relational capital (RC) is adopted. A traditional IC value tree which shows the hierarchy and relative importance of the chosen IC components (i.e., the subset of HC, SC and RC), is also used as a framework to identify the knowledge inventory that is important or of relevance to the company. However, conventional approaches in the construction and the elicitation of the IC components need intensive time and manpower, require experienced IC management practitioners with facilitation skills, and introduce unavoidable subjectivity and human bias to the study result. Therefore, this research study offers an alternative choice to companies to overcome such limitations. In this study, an IC oriented knowledge asset elicitation system (iCOKES) was developed based on text mining technology to reveal the relevant IC components. An IC thesaurus model was also built with an IC domain dictionary and taxonomy in iCOKES to discover the important IC components that frequently appear in various sources of unstructured information. The IC components were extracted and their relative importance calculated according to corresponding frequency of appearance in the various documents being analysed.
The developed IC driven knowledge audit methodology was validated through a trial implementation in a selected reference site, a public utility company named The Hong Kong and China Gas Company Limited (Towngas), and an interview was conducted with the stakeholders of a selected reference site to validate the IC value tree obtained from the semi-automatic text mining. These IC components are presented in a structured IC value tree which serves as a template for the examination of knowledge activities, knowledge inventory and knowledge flow in a company. The IC driven knowledge audit methodology developed in this project is demonstrated to be a more efficient approach for eliciting IC items objectively with a minimum amount of human intervention or human bias in the construction of the IC value tree. The method has the capability to elicit IC components from a large amount of unstructured information which can by any text based material, e.g. annual reports, meeting notes, forum discussion or instant messages, etc. The IC value tree is aligned with an audit goal. This can not only ensure that every knowledge inventory and flow supports and maximizes the IC goal, but also renders a good linkage between IC and the results of a knowledge audit. This project is the first of its kind to integrate IC and knowledge audit research in the development of a semi-automatic method in the classification of IC and the automatic generation of an IC value tree for carrying out the knowledge audit in a company.
Hong Kong Polytechnic University -- Dissertations
|Pages:||xvi, 200 p. : ill. ; 30 cm.|
|Appears in Collections:||Thesis|
View full-text via https://theses.lib.polyu.edu.hk/handle/200/7210
Citations as of Oct 1, 2023
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