The research uses neural nets as a tool in the investigation of busienss failure prediction and business performance monitoring. The novelty lies in the introduction of models including qualitative factors as well as financial ratios. In addition, an analysis of data gathered from a new survey is offered. To achieve its objectives the research begins by exploring the AI options and then reviews current neural net technology with a view to identifying appropriate technology for the implementation of a classifier for the two areas of failure prediction and performance monitoring. After consideration of the strengths and weaknesses of the options, a multi-layer perceptron, back propagation net is adopted as being unsuitable for this application. In order to verify the validity of the bespoke neural net software it was necessary to employ a two stage strategy. The first step was to confirm that the net, as implemented, retained the expected property of being able to solve problems that were not linearly separable. This was achieved by demonstrating its ability to solve the straightforward XOR problem. To be confident of the net performance it was deemed necessary to replicate the experiments of previous research which used only purely financial inputs to the net. The results confirmed the validity of the new network implementation. Using the intital results as a control, experiments were undertaken to ascertain the effect of reducing the training sample size and to identify minimum sample sizes commensurate with maintaining the effectiveness. The work then further contributes to this research by using traditional stastical methods to provide an empirically derived equation for calculating the minimum number of training patterns required for corporate failure prediction in the context of the experimental sets of variables. The resulting failure prediction model was then used to test for symptoms of bankruptcy in firms currently trading. The thesis then leads on to describing a technique developed in this study for pre-processing qualitative questionnaires, prior to input into a neural model as well as providing a method for predicting values not supplied in incomplete survey responses. A contribution is also made to the area of company performance analysis by using neural techniques and discriminant analysis to show that relationships do exist between certain company variables and business performance, as well as highlighting which of these variables are the most important if an appropriate corporate condition monitoring strategy is to be developed. Lastly, the corporate performance neural network model is enhanced by facilitating the categorisation of a firm into one of several performance bands.

Knowledge Management Systems are deployed in organisations of all sizes to support the coordination and control of a range of intellectual assets, and the low cost infrastructures made available by the shift to ‘cloud computing’ looks to only increase the speed and pervasiveness of this move. However, their implementation has not been without its problems, and the development of novel interventions capable of supporting the mundane work of everyday organisational settings has ultimately been limited. A common source of trouble for those formulating such systems is said to be that some proportion of the knowledge held by a setting’s members is hidden from the undirected view of both The Organisation and its analysts - typically characterised as a tacit knowledge - and can therefore go unnoticed during the design and deployment of new technologies. Notwithstanding its utility, overuse of this characterisation has resulted in the inappropriate labelling of a disparate assortment of phenomena, some of which might be more appropriately re-specified as ‘hidden knowledges’: a standpoint which seeks to acknowledge their unspoken character without making any unwarranted claims regarding their cognitive status. Approaches which focus on the situated and contingent properties of the actual work carried out by a setting’s members - such as ethnomethodologically informed ethnography - have shown significant promise as a mechanism for transforming the role played by members’ practices into an explicit topic of study. Specifically they have proven particularly adept at noticing those aspects of members’ work that might ordinarily be hidden from an undirected view, such as the methodic procedures through which we can sometimes mean more than we can say in-just-so-many-words. Here - within the context of gathering the requirements for new Knowledge Management Systems to support the reuse of existing knowledge - the findings from the application of just such an approach are presented in the form of a Pattern Language for Knowledge Management Systems: a descriptive device that lends itself to articulating the role that such hidden knowledges are playing in everyday work settings. By combining these three facets, this work shows that it is possible to take a more meaningful approach towards noticing those knowledges which might ordinarily be hidden from view, and apply our new understanding of them to the design of Knowledge Management Systems that actively engage with the knowledgeable work of a setting’s members.