Khuliso Sigama

This paper applies a Convolutional Neural Network (CNN)-based vehicle security enhancement model, with a specific focus on the South African context. While conventional security systems, including immobilizers, alarms, steering locks, and GPS trackers, provide a baseline level of protection, they are increasingly being circumvented by technologically adept adversaries. These limitations have spurred the development of advanced security solutions leveraging artificial intelligence (AI), with a particular emphasis on computer vision and deep learning techniques. This paper presents a CNN-based Vehicle Security Enhancement Model (CNN-based VSEM) that integrates facial recognition with GSM and GPS technologies to provide a robust, real-time security solution in South Africa. This study contributes a novel integration of CNN-based authentication with GSM and GPS tracking in the South African context, validated on a functional prototype.The prototype, developed on a Raspberry Pi 4 platform, was validated through practical demonstrations and user evaluations. The system achieved an average recognition accuracy of 85.9%, with some identities reaching 100% classification accuracy. While misclassifications led to an estimated False Acceptance Rate (FAR) of ~5% and False Rejection Rate (FRR) of ~12%, the model consistently enabled secure authentication. Preliminary latency tests indicated a decision time of approximately 1.8 s from image capture to ignition authorization. These results, together with positive user feedback, confirm the model’s feasibility and reliability. This integrated approach presents a promising advancement in intelligent vehicle security for regions with high rates of vehicle theft. Future enhancements will explore the incorporation of 3D sensing, infrared imaging, and facial recognition capable of handling variations in facial appearance. Additionally, the model is designed to detect authorized users, identify suspicious behaviour in the vicinity of the vehicle, and provide an added layer of protection against unauthorized access.

Background and Aim of Study: South African banks are generally known for early technology adoption. While this is so, there is a need to integrate some of the fourth industrial revolution technologies such as big data analytics and cloud computing collectively referred to as cloud-based big data analytics; and subsequently consider technology related aspects required for adopting integrated technologies of this nature. The aim of the study is to identify technology related factors that are necessary for adopting cloud-based big data analytics in South African banking. Material and Methods: A qualitative research approach was followed as well as an interpretivism paradigm and a single case study research strategy. Semi-structured interviews were employed for data collection from eleven professionals in the Information Technology division of a South African bank. Results: In total, 35 technology factors required for adopting cloud-based big data analytics were identified in this study and furthermore categorized into; internal cloud-based big data analytics criteria, cloud-based big data analytics capabilities or skills, cloud-based big data analytics data integrity levels, data security and readiness for adopting cloud-based big data analytics and cloud-based big data analytics external criteria. Conclusions: The results of this study could imply that the adoption of cloud-based big data analytics in the banking sector takes into consideration an outsourcing model or setting. In this structure, technology factors are not only specific to the bank concerned. The banking sector has its own technology requirements that banks are expected to adhere to or take into consideration, while some technology factors could only be addressed by the cloud-based big data analytics service providers. The identified factors could be used in the conceptualization of a cloud-based big data analytics framework in future research.

The digital transformation has employed a variety of cloud-enabled technologies that have influenced changes in products and services. This have seen the advent of cloud Enterprise Resource Planning systems. Despite the hype over the cloud's perceived benefits, cloud ERP (CERP)are not a fit for all organizations especially those that need maximum customization. As a result, some organizations especially in are not comfortable migrating their services and applications to the cloud. This paper sought to investigate and develop a model for CERP adoption in developing countries institutions of higher learning. Findings of this study indicate that, vendor and organizational factors are more important determinants of CERP adoption. This study recommends the assessment of the interacting effects of the users' demographics that could impact on adoption with change in time as well as the sensitization, training and involvement of users during CERP adoption.

Massive Open Online Courses (MOOCs) are free online courses available for different categories of learners to enrol on different courses of their choice that allow them unrestricted participation. MOOCs provide a platform for interactive forums and is essentially known for self-paced learning, flexibility, and interactivity. However, for MOOCs to be well acknowledged by all learners regardless of their social economic backgrounds, they should be provided at platforms that provide a good learning experience at low bandwidth and offline. Much as this is so, fewer organizations have leveraged MOOCs for corporate training to enrich their employees needs and customized to fit their environments. The main aim of this study was to develop a model for the adoption of MOOCs for corporate training in private sector which model will help to serve as a proper guideline and support to enable organizations to use it for training purposes to enhance skills needed for effective execution of duties. Several private organizations were used for data collection and data was quantitatively analysed. The model was validated using inferential statistics and descriptive analysis. The results indicated that perception and attitude toward technology, teaching styles, and technology control are key influencing variables of MOOCs for corporate training. This study contributes in extending the importance of MOOCs for corporate training. By developing a model to address the adoption and use of MOOCs for corporate training, provide insights into the appropriate MOOCs platforms, and develop guidelines for corporate training in all organisations.

Background: Software development teams are challenged with the adoption of the most appropriate software development methodology. Despite their acceptance of agile methodologies, still many countries use traditional software development methods.Objectives: The major objective of this study was to develop a contextualised model for the adoption of agile methodologies in software development teams in South Africa.Method: Our study identified the factors that contributed to the adoption of agile software development teams in South Africa by conducting a factor analysis. We used descriptive statistics to determine the frequencies of the participant’s demographics and situational variables. A composite research model based on technology, organisation and environment, individual characters and culture constructs was constructed and analysis was performed by applying the statistical techniques of correlation analysis, regression analysis and structural equation modelling.Results: The result of the analysis revealed that many teams do not have a specific model that they use in agile development methodology. Technology, organisation, environment and culture constructs were found to have an impact on teams in adopting agile methodologies.Conclusion: The study demonstrated that organisations lack appropriate models of agile methodologies adoption to inform their decision making. Therefore, adopt the developed model in taking informed decisions for their software development methodologies. The study makes a practical contribution to management and practice.Contribution: This study improves on software development approaches in developing countries. The developed model can be adopted as a theory in future research. It contributed literature to the body of knowledge.

The global pandemic of coronavirus “Covid-19” in the fall of 2019 caused dramatic changes in all sectors, including education. Businesses, like corporate organizations that normally train their employees annually, were equally affected, due to lack of instructors. Learning institutions were faced with various challenges due to the abrupt paradigm shift of changing from face-to-face to online teaching and learning, with limited preparedness. For scalability, and to maintain their annual strategies of keeping their dynamic capabilities, organizations were forced to adapt online training although with limited preparedness. However, electronic learning innovations such as the massive open online courses (MOOCs) that should have been leveraged by corporate organizations to train their employees during these hard times, had been mainly tailored for educational institutions. More still, much as MOOCs are essential for all enterprises, there have been few or no models developed to inform their adoption for corporate training. This study sought to bridge this gap, thus developed a model for MOOCs adoption for corporate training, based on data collected from South African private sectors, and analysed quantitatively. Results indicated that perception and attitude toward technology, teaching styles, and technology control are key influencing factors of MOOCs adoption for corporate training. The developed model will be used to provide insights into MOOCs adoption for workplace training, and to extend research on MOOCs usage. It is recommended that future studies consider the analysis of the moderating effects of respondents’ demographics, their perceptions possibly changing over time.