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Building knowledge absorptive capacity in South African public companies through recruitment practices
(Academic Conferences International Ltd, 2023-01-01) Phaladi, Malefetjane Phineas
Most state-owned companies (SOCs) in South Africa are in a perpetual struggle to recruit human resources and replenish their intangible knowledge asset losses that are largely due to high employee turnover. The study is interdisciplinary in nature, presenting a strong link between recruitment practice, employee turnover, knowledge loss and knowledge absorptive capacity. The research project used a mixed methods exploratory sequential design by gathering in-depth qualitative data through interviews with 20 human resource managers in 9 SOCs. Survey data was collected from a 25% response rate to the 585 distributed questionnaires. The survey instrument was tested for reliability with a Cronbach’s alpha at 0.94. The qualitative data extracted through the interviews were analyzed thematically using Atlas.ti software, whilst the quantitative data were analysed using Statistical Analysis Software (SAS). The findings revealed that due to increased human resources mobility, human resource management (HRM) establishments in many SOCs were in a perpetual struggle to fill vacancies in mission-critical areas. Knowledge-loss induced by human resource attrition was a serious challenge in most SOCs. On a positive note, the study revealed that recruitment practices were knowledge-driven, largely because they supported SOCs in the sourcing of the required company-specific human and knowledge resources, albeit some extant challenges. The study concluded that HRM recruitment practices build knowledge absorptive capacity in South African SOCs.
HRM alignment and integration in knowledge risk management in South African state-owned enterprises
(Academic Conferences International Ltd, 2024) Phaladi, Malefetjane Phineas
Most state-owned enterprises (SOEs) in South Africa face serious challenges with tacit knowledge loss risks, largely due to a lack of alignment and integration points for human resource management (HRM) practices in knowledge management to effectively manage such knowledge loss risks. This study was interdisciplinary in nature, presenting empirical evidence of a strong connection between HRM and knowledge management (KM), especially its knowledge risk management (KRM) sub-domain. The research paper employed a qualitative research design, which involved collecting in-depth qualitative data through interviews with 20 human resource (HR) managers in 9 SOEs. The qualitative data extracted through the interviews were analyzed thematically using Atlas.ti software. The research results revealed that HRM practices are not aligned, integrated and focused on mitigating enterprise tacit knowledge loss risks in most South African SOEs. A lack of HRM alignment and integration was a serious issue across the board, irrespective of whether the participating SOEs had knowledge management structures, roles and strategies in place. However, on a positive note, HR managers acknowledged the need for the integration and alignment of HRM strategies regarding effective knowledge loss risk management. The study highlights a deficiency in HRM alignment and integration for effective knowledge loss risk management. The researcher infers that HRM alignment and integration in KRM has a critical strategic and operational role to play in the mitigation of enterprise knowledge risks, as well as in developing the required knowledge management capabilities. The study sought to close a knowledge gap in the existing body of knowledge by presenting empirical evidence identifying alignment and integration points of HRM strategies into KRM for SOEs to effectively reduce knowledge loss risks. Furthermore, the study presents a strong link between HRM and KRM in building KM capacity for the reduction of the risks associated with tacit knowledge loss.
A split-then-join lightweight hybrid majority vote classifier
(Springer International Publishing, 2022) Gadebe, Moses L.; Ojo, Sunday O.; Kogeda, Okuthe P.
Classification of human activities using smallest dataset is achievable with tree-oriented (C4.5, Random Forest, Bagging) algorithms. However, the KNN and Gaussian Naïve Bayes (GNB) achieve higher accuracy only with largest dataset. Of interest KNN is challenged with minor feature problem, where two similar features are predictable far from each other because of limited number of classification features. In this paper the split-then-join combiner strategy is employed to split classification features into first and secondary (KNN and GNB) classifier based on integral conditionality function. Therefore, top K prediction voting list of both classifier are joined for final voting. We simulated our combined algorithm and compared it with other classification algorithms (Support Vector Machine, C4.5, K NN, and Naïve Bayes, Random Forest) using R programming language with Caret, Rweka and e1071 libraries using 3 selected datasets with 27 combined human activities. The result of the study indicates that our combined classifier is effective and reliable than its predecessor Naïve Bayes and KNN. The results of study shows that our proposed algorithm is compatible with C4.5, Boosted Trees and Random Forest and other ensemble algorithms with accuracy and precision reaching 100% in most of 27 human activities.