Full metadata record
|dc.contributor||Department of Industrial and Systems Engineering||en_US|
|dc.contributor.advisor||Lee, W. B. (ISE)||-|
|dc.contributor.advisor||Tsui, Eric (ISE)||-|
|dc.creator||Tsang, Wing Chi||-|
|dc.publisher||Hong Kong Polytechnic University||-|
|dc.rights||All rights reserved||en_US|
|dc.title||Knowledge risk factor assessment : design and evaluation||en_US|
|dcterms.abstract||Knowledge plays a more important value-creation role in the new economy and is the critical success factor of most enterprises today. Knowledge management (KM) is a discipline to study the acquisition, storage, organization, retention and sharing of knowledge in an enterprise or organization. When the organizational knowledge is mismanaged, it can cause problems or serious threats to the operation or even the sustainability of an enterprise. On the other hand, enterprise risk management (ERM) has long focused on physical or financial risk rather than intangible risk like loss of knowledge which is equally important for the survival of an organization. Therefore, this research was a natural outgrowth of the two disciplines of recent times in an attempt to address what is lacking in both when it comes to knowledge-related risk. It was an exploratory research and adopted an evidence-focused approach built on the principles derived from KM and ERM. There were two main parts in this study. First, the status and awareness of how knowledge risk is handled in enterprises and what perceptions of knowledge risk staff in an organization held were explored. This was achieved by conducting semi-structured interviews with KM professionals. Secondly, the research moved on to develop a method called the KRFAM (Knowledge Risk Factor Assessment Model) to assess the risk factors of several common kinds of knowledge risk which were identified in the forgoing interview. The evidence-focused model consisted of two levels of assessment of risk factors. The Level 1 assessment was carried out through a survey approach. In the Level 2 assessment, to arrive at data which were more evidence-based, the survey participants were asked to recall relevant facts, data and information before attending a face-to-face interview with the researcher who then guided and assisted participants to go through the evidence to give a belief degree. The collected assessment data from both levels were then processed by the Evidential Reasoning (ER) approach, to provide adjusted ratings, which therefore reflected more reliably the actual situation. The model was pilot tested in three companies of different business natures related to toys and consumer products, construction, and energy industries. They came up with various ratings on their knowledge risk factors which were expected, and different degree of mismatch between their perceived ratings (from Level 1 assessment only) and adjusted ratings (which were the modelling results from both the Level 1 and Level 2 assessments under the ER approach). The discrepancies could be explained by a better understanding of the risk factors and the evidence from their company practice, of which they might not be have been aware before the Level 2 assessment. All of them found the assessment to be useful and it enabled them to have better insights into the knowledge risk factors involved. The evidence-focused approach adopted has made the knowledge risk factor assessment more reliable and could be a valuable tool for companies to identify and launch their KM initiatives.||en_US|
|dcterms.extent||200 pages : color illustrations||en_US|
|dcterms.LCSH||Hong Kong Polytechnic University -- Dissertations||en_US|
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