The importance of knowledge sharing in healthcare organisations cannot be overemphasised. This is mainly due to the dynamic and sensitive nature of knowledge within the healthcare sector that can assist in caring for the patients and other administrative operations. The absence of knowledge sharing and retention practice in healthcare organisations may result in negative repercussions. Hence, using Actor-Network Theory, the study will investigate and identify the factors influencing the current knowledge sharing taking place within the South African healthcare system. One of the top healthcare organisations was selected for data collection, and a qualitative research method was adopted in this study. The findings revealed the different actors involved in the sharing of knowledge and the networks that enable knowledge sharing and retention. The study also highlights the factors that triggered knowledge sharing.

Data are more rapidly generated than they can be digested and consumed in real time due to the increased mobility and automation of activities of small and medium enterprises (SMEs). These challenges could be avoided if SMEs in developing countries deployed Mobile Business Intelligence (MBI) within their settings. Though the deployment of MBI in SMEs in developed countries has been noticeable, its success in the developing countries is still far from reach. Few to no studies have been conducted to investigate the direct and indirect factors influencing the deployment of MBI within SMEs in developing countries. This study sought to investigate and determine factors that influence the deployment of MBI in developing countries’ SMEs. Relevant literature was reviewed to determine factors that previous researchers have identified as essential in MBI deployment. Textual analysis was used to verify each factor, remove repetitions and determine the frequencies of their appearance in the literature. Principal Component Analysis (PCA) was then used as reduction method for the many factors that had been identified in the literature. This also helped in the factor categorization. Lastly, a multi-criteria decision-making method of Advance Impact Analysis (ADVIAN^®) was carried out to determine the direct and indirect impacts that exist between the factors and the overall system of MBI deployment. Based on the ADVIAN^® results, impacting factors were classified into four categories namely: active (for example, user privacy), reactive (for example, changing trends), critical (for example, top management support) and inert (for example, technician’s location). The findings of this study are expected to be integrated into SMEs in developing countries to prioritize those factors that need additional attention during the deployment of MBI.

Background: Public sectors generate large data volumes, necessitating improved data management and governance. Economic, political, and technological challenges hinder data management, leading to inefficiencies in service delivery. In South Africa, service delivery problems are exacerbated by a lack of accountability, weak leadership, poor procurement practices, and ineffective human resource management. Despite investments in information communication technology (ICT), data management challenges persist, especially with the rise of semi-structured and unstructured data. Public sectors must treat data as a strategic asset for governance, ensuring timely, reliable, and organised data to meet citizens’ needs. Objectives: This study aimed to identify factors influencing Big Data governance to enhance service delivery in the Limpopo Vhembe municipality, South Africa. Method: A mixed-method approach was used, including a closed-ended questionnaire and qualitative validation. Simple random sampling targeted information technology (IT) professionals and data-related personnel. Descriptive statistics analysed respondent demographics and situational variables. Results: Data quality management, such as data quality principles, analysis, planning, quality assurance, and control, were found to be critical for effective Big Data governance. These elements help increase trust in data, improve productivity, reduce costs, ensure compliance, and enhance decision-making and innovation, ultimately enabling citizen-centric services. Conclusion: This study reaffirmed the potential of enhancing decision-making in Big Data governance by uncovering additional insights and implementing proactive strategies to address present and near-future opportunities and challenges. Contribution: The study contributes to the field by addressing the gap in existing Big Data governance environment within the public sector. Keywords: Big Data; Big Data governance; public sector; factors; enhancing; decision-making; technology.

Information supports the smooth operations and vital decision-making endeavours facilitating smooth and seamless business process execution within an enterprise. As a result of increasing mobility and automation of activities within enterprises, huge amounts of data are rapidly generated than they could be instantaneously utilized in heterogeneous, intra or inter-organisational business processes. Developing countries' Small and Medium Enterprises (SMEs) are faced with challenges of accessing intelligent information for decision making at different operational sites. SMEs in developing countries are paying a huge business opportunity cost by not utilizing Mobile Business Intelligence (MBI) systems. This is as a result of lack of MBI framework to inform the deployment of MBI solutions in developing countries' SMEs. This study therefore is envisioned to design a framework for the deployment of MBI in developing countries' SMEs. So as to achieve this, the study adopted various scientific approaches (Textual Analysis, Principal Component Analysis, and Structural Equation Modelling) systematically. This study is expected to contribute towards the literature and the methods in establishing and determining the factors needed for the development of frameworks in Information Systems studies. Practically, the study is expected aid deployment of MBI for SMEs in developing countries' contexts.