Marist International University College, Nairobi – Kenya is challenged with problems like duplication of work due to lack of a central repository for knowledge, loss of knowledge through expertise leaving the institution without knowledge being captured and over reliance on a few known subject experts as others have not been identified. This research thus set out to address these problems. The aim of the study was to conduct a knowledge management assessment at the Marist International University College (MIUC) in order to identify and recommend a suitable strategy for the institution. The study employed a concurrent triangulation mixed methodology approach which encompassed a questionnaire and an interview schedule to collect data from 33 academic staff and 9 members of the MIUC management respectively. These two groups were purposively selected as the target population for the study as they play the key role in knowledge creation at MIUC. All 33 copies of the questionnaires that were distributed to the teaching staff were returned by respondents and all the 9 MIUC members of management were successfully interviewed. Data transformation analysis was applied during which qualitative data from open-ended questions and interviews were quantified using content analysis. Quantitative data in the questionnaires was descriptively analysed using SPSS. The study revealed a variety of informal knowledge management structures and resources at MIUC and the challenges of managing knowledge at Marist. The main challenge was that there was no uniformity and consistency in the management of knowledge. The study hence, formulated a KM strategy for MIUC that would help leverage its knowledge assets. / Information Science / MA (Information Science)
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:unisa/oai:uir.unisa.ac.za:10500/18310 |
| Date | 02 1900 |
| Creators | Anduvare, Everlyn Mmbone |
| Contributors | Minishi-Majanja, M.K. Prof |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Format | 1 online resource (xiii, 215 leaves) |
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