Factors influencing Internet banking adoption in South African rural areas

Background: Social media comprise technologies that facilitate learning in higher education institutions. However, many first-year students at tertiary education institutions are not taking advantage of social media for their learning because of environmental and personal factors related to the digital divide (DD).Objectives: The objective of this research study was to investigate the impact of the DD factors on first-year students in using social media for learning in tertiary education institutions.Method: A survey method was used to conduct the study. Social cognitive theory was employed as a theory underpinning this research. A questionnaire technique was used to collect data from 600 first-year students of a multi-campus university. Three hundred students came from each of the two campuses. Regression analysis was performed with the purpose of testing the hypotheses of the study.Results: The result of the analysis revealed a low computer access and usage but a high percentage of mobile devices usage by students from disadvantaged backgrounds. Personal factors were found to have an impact on the behaviour of students in adopting social media for their studies. The study also found that the prevalence of social media nullifies the lack of computer resources and connection to the Internet in disadvantaged areas.Conclusion: This study demonstrated that the DD was more complex than hitherto envisaged. The study recommends that ownership of computers and devices connected to the Internet needs to be promoted, especially in disadvantaged areas.

Background: Big data analytics in education is a new concept that has the potential to change the decision-making landscape in South African Colleges. Higher institutions of learning, including Technical and Vocation Education Training (TVET) colleges like all other organisations, rely on data for their decision-making. These decisions affect the way pedagogy and student management is administered. Colleges collect huge quantities of data in different formats from students, staff and stakeholders for different reasons and occasions.Objectives: The goal of this study was to investigate how Big Data analytics and their tools may improve decision making in TVET colleges in South Africa through the lens of actor-network theory (ANT).Method: A qualitative, interpretive inquiry was undertaken. A case study using focus group was conducted. The data collected through interviews were arranged into themes and a thematic approach was employed to analyse these themes using QDA Miner Lite software.Results: The results from focus group interviews revealed that TVET colleges collect an enormous amount of data. These data are extracted for different reasons, yet there are no Analytics used for decision-making. Decisions are made by the highest-paid individuals (HiPPO) in colleges.Conclusion: This dissertation recommends that the TVET colleges invest in data science skills for their staff, and Big Data infrastructure. Big Data technologies such as Mongo DB and Hadoop are recommended as the most commonly and advanced tools that can be used for Big Data analytics.

Background: The Internet of Things (IoT) interconnects humans to things, things to things, and humans to humans, streamlining routine activities via the Internet for time and cost savings, and heightened productivity. Despite a global uptick in IoT adoption, Limpopo province’s agricultural sector faces a significant lag in embracing and utilizing IoT technologies. Objectives: The goal of the study was to determine the factors that may determine the adoption and use of IoT within the agricultural sector in Limpopo province. Method: A qualitative study was carried out using Unified Theory Acceptance and Usage of Technology (UTAUT) as the theoretical lens. Data were collected using interviews, observations, and document reviews from the Limpopo agricultural sector participants. Analysis was performed by means of thematic analysis. Results: Priori themes for this study namely, organisational support, cost, knowledge gap, security and policy were confirmed as significant in the adoption of IoT in the Limpopo agricultural sector. Monitoring, control, perceived value, and risk emerged as new themes in the adoption of IoT. Conclusion: For Limpopo agricultural sector to be transformed from manual methods of conducting agricultural activities to the automated method, they will need support enshrined in policies dealing with cost, knowledge transfers as well as security assurances about the use of modern technology such as IoT. Contribution: A study exploring IoT adoption in Limpopo’s agricultural sector promises regional transformation. The Internet of Things can revolutionise practices with real-time monitoring, data-driven decision-making, boosting crop yields, resource efficiency, and sustainability. It empowers smallholder farmers, stimulates rural development, offers valuable data for policymakers, and enhances economic viability in addressing challenges like water scarcity, climate change, and market competitiveness, ultimately improving livelihoods in Limpopo province.

Software as a Service (SaaS) seems to be the most attractive solution among cloud services, but there is a low adoption rate among South African Small Medium Enterprises (SMEs). The aim of this study was to investigate the significant factors hindering the adoption of SaaS among South African SMEs. The study adopted Diffusion of Innovation Theory (DOI) as a theoretical basis. Data was collected through questionnaires and analyzed using regression analysis. The study findings indicate that some SMEs have not used SaaS because they have never heard of it. But may consider SaaS solutions if it reduces the organization's IT expenditure. For those considering using SaaS, complexity and cost of SaaS were the most significant factors determining their potential for the adoption.

The accuracy of malware detection is closely related to the available datasets, which are often small and imbalanced. To overcome these challenges, this study proposed a new method that creates synthetic malware data and increases the size and balance by generating several data sets with a flow-based model. Subsequently, a random forest classifier is fitted on this augmented dataset. This study aimed to analyze the generation of synthetic data based on flow-based models and the impact of synthetic data generation on the performance of a random forest for malware detection. A flow-based model was used to generate a balanced synthetic dataset based on the CICMalDroid2020 dataset. The generated data was used for feature selection and engineering to optimize the Random Forest model. The experimental results demonstrate the effectiveness of the proposed approach. The flow-based model generated an additional 13,402 samples, massively increasing the dataset size, even though the original dataset had only 11,598 data entries. After training on the synthetic augmented dataset, the Random Forest model achieved better performance compared to the original dataset evaluation with metrics precision (93%), recall (100%), balanced precision (96%), and the F1 score (91%). The results show that flow-based model-generated synthetic data can significantly enhance malware detection capabilities.