The aim of this study is to develop a model for knowledge synchronisation in organisations. The research aim is further broken down into two research objectives that are handled during this study: • Examine and measure the gap between a typical organisation and a Learning Organisation and the corresponding organisation’s Subjective Knowledge and Knowledge Management Systems, and • Examine and create appropriate models and methods to synchronise organisation’s Subjective Knowledge and Knowledge Management Systems. This research attempts knowledge synchronisation in view of creation and maintenance of Learning Organisations. This study combines three broad areas in an organisation: Learning Organisations, Intellectual Capital, and Knowledge Management Systems. This research proposes a new organisational epistemology in the context of the Subjective and Objective Knowledge. The organisational ontology consists of five hierarchical layers: observation, data, information, knowledge and wisdom. Wisdom and observations, being embodied, are subjective in nature and they are referred to as Subjective Knowledge throughout the thesis. Data, information and knowledge of an organisation, being objective in nature, are contained in Information Systems or Knowledge Management Systems; and throughout the thesis they are referred to as Objective Knowledge. The significance of this research and its major contribution resides in the development and validation of a comprehensive model for Subjective - Objective Knowledge synchronisation, with a view of creation and maintenance of Learning Organisations. A Knowledge Synchronisation Model (KSM) has been proposed to measure the gap between a typical organisation and a Learning Organisation. Furthermore, KSM also deals with the gap between an organisation’s Subjective Knowledge and Knowledge Management Systems. A web-based survey has been conducted to validate the proposed Knowledge Synchronisation Model. The unit of analysis has been ‘an organisation’ with Knowledge Management initiatives. Snowball sampling technique has been used to contact such organisations and five hundred and ten responses have been received. Four hundred and seventy responses have been considered for analysis. Responses have been classified into four clusters: Learning Organisations, whose Subjective Knowledge and Knowledge Management Systems have been in sync, Technology oriented organisations with high Knowledge Management Systems and relatively low Subjective Knowledge, People oriented organisations with high Subjective Knowledge and relatively low Knowledge Management Systems, and finally, the organisations with no Knowledge Management strategy. Regression analysis has been used to validate the hypotheses. The orientation towards technology or people will present itself as missing organisational characteristics. Two organisations from the survey participants have been selected for knowledge synchronisation through Action Research Studies. The first organisation has been identified as technology oriented and lacking organisational ‘Awareness’ and ‘Systems Thinking’. A Community of Practice (CoP) and a knowledge portal have been suggested to the first organisation for knowledge synchronisation. The second organisation has been identified as people oriented and lacking ����Personal Mastery����. A Community of Practice (CoP) and a knowledge base have been suggested to the second organisation. The limitation posed by the sampling technique ‘snowball sampling’ is a significant limitation in this research. This research does not consider the effects of location and investor capital on the proposed model. This is another limitation of this research. This research has academic implications for the theories of Learning Organisations, Intellectual Capital and Knowledge Management. Further investigations will be necessary to study the effects of location and investor capital, human related issues such as trust and culture, and the latest technologies such as web 2.0 and mobile devices, on the proposed model. / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:ADTP/201147 |
| Date | January 2008 |
| Creators | Lakkaraju, Sai Kiran, University of Western Sydney, College of Health and Science, School of Computing and Mathematics |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
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