This research was set out to investigate the role of knowledge management as a coping strategy for PHE institutions in Botswana, especially given that they operate in a highly regulated environment. One of the major drivers of volatility in the educational sector is intensely volatile regulatory environment in which the institutions operate. Further, a large portion of the stakeholder community of these institutions hold a strong believe that these institutions offer poor quality education to maximise profit. The primary objective of this study is therefore to determine the role of knowledge management (KM) enablers in facilitating KM practices in selected PHE institutions in Botswana that operate in this highly regulated environment and to develop a model for effective KM in these institutions. The study adopted a survey research design and collected quantitative data through a structured self-administered questionnaire and document reviews. The subjects comprised all five degree-awarding PHE institutions, which were strictly regulated by the Tertiary Education Council (TEC). The population surveyed came to 670 and sample size was 350. Data was analysed through various statistical measures such as Structural Equation Modelling (SEM) in the form of Analysis of Variance (ANOVA), multiple regression analysis, and Chi-square test. The results of the study revealed that KM enablers were playing an insignificant role in facilitating KM practices in selected PHE institutions in Botswana. Results of the study can be generalised to similar institutions elsewhere operating in similar environments. In order to enhance KM practices in PHE institutions, it is recommended that the institutions adopt a systematic approach to KM, establish an organisational culture and structure that promote KM practices, and enhance the quality of their human capital including leadership. It should be noted that the state of KM in organisations operating in an uncertain environment can be enhanced if the leadership carefully controls the family-owned setting and organisational culture as these factors can detract from the organisation’s effective practising of KM. However, strategic leadership, organisational structure, and the role played by stakeholders played positive deterministic factors in ensuring an enhanced KM drive. / Business Management / D.Admin. (Business Management)

Model and variable selection have attracted considerable attention in areas of application where datasets usually contain thousands of variables. Variable selection is a critical step to reduce the dimension of high dimensional data by eliminating irrelevant variables. The general objective of variable selection is not only to obtain a set of cost-effective predictors selected but also to improve prediction and prediction variance. We have made several contributions to this issue through a range of advanced topics: providing a graphical view of Bayesian Variable Selection (BVS), recovering sparsity in multivariate nonparametric models and proposing a testing procedure for evaluating nonlinear interaction effect in a semiparametric model. To address the first topic, we propose a new Bayesian variable selection approach via the graphical model and the Ising model, which we refer to the ``Bayesian Ising Graphical Model'' (BIGM). There are several advantages of our BIGM: it is easy to (1) employ the single-site updating and cluster updating algorithm, both of which are suitable for problems with small sample sizes and a larger number of variables, (2) extend this approach to nonparametric regression models, and (3) incorporate graphical prior information. In the second topic, we propose a Nonnegative Garrote on a Kernel machine (NGK) to recover sparsity of input variables in smoothing functions. We model the smoothing function by a least squares kernel machine and construct a nonnegative garrote on the kernel model as the function of the similarity matrix. An efficient coordinate descent/backfitting algorithm is developed. The third topic involves a specific genetic pathway dataset in which the pathways interact with the environmental variables. We propose a semiparametric method to model the pathway-environment interaction. We then employ a restricted likelihood ratio test and a score test to evaluate the main pathway effect and the pathway-environment interaction. / Ph. D.