https://ir.unisa.ac.za/handle/10500/2748 2026-09-19T00:57:37Z https://ir.unisa.ac.za/handle/10500/32950 Development of a framework for ethical and cyber-safe robotics in the service industry Mordi, Emmanuel Ifie The increased use of service robotics in sectors like hospitality, healthcare, and logistics presents opportunities but also challenges in cyber-safety, ethics, and human-robot interaction (HRI). While progress has been made, current frameworks are fragmented or focus too much on technical efficiency, neglecting ethics, social issues, and data protection. This is especially true in emerging economies where imported robots often conflict with local cultural and regulatory standards. This study introduces a comprehensive conceptual framework called the Socio-Tactical Integrated Framework to guide ethical, cyber-safe, and contextually suitable deployment of service robotics (ACERBOTICS). A Design Science Research approach was used to develop a structured theoretical model that addresses gaps in the current research. The study includes a systematic literature review, which provides a basis for identifying and understanding existing critical gaps in the literature and design criteria for a proposed research artefact to address the gaps. Following the development of the research artefact, an expert evaluation was conducted using a questionnaire with both closed and open-ended questions to assess the relevance, clarity, applicability, and ethical robustness of the proposed framework. Expert evaluation (n = 47) demonstrated high coherence, ethical robustness, and practical feasibility, while also identifying areas of improvement, including contextual adaptability and operational scalability. These insights informed the development of a refined framework that integrates ethical governance, cyber-safety resilience, socio-technical alignment, and human-centred design principles. This study proposes a unified, theoretically grounded, and practically applicable framework for governing autonomous service robotics in diverse socio-technical environments. The proposed framework provides a basis for future empirical validation and policy development, particularly in African and other emerging-economy contexts. By integrating cyber-safety, ethics, and service robotics, this research advances scholarly understanding and promotes responsible innovation in the field of intelligent robotic systems in the service industry. 2026-02-27T00:00:00Z https://ir.unisa.ac.za/handle/10500/32937 A framework for end-user compliance with the information security policy in government institutions Lekhuleni, Sakhile Charity Dories Employees’ non-adherence causes most security breaches in both private and public organisations to information security policies (ISPs). The purpose of this research is to develop a framework for end-user compliance with ISPs in government departments. A framework that integrates all factors that enhance end-user compliance will assist organisations and information security managers in understanding the elements that affect end-user compliance. A systematic literature review was conducted using the PRISMA method to identify the factors and components contributing to the development of the framework. Moreover, semi-structured interviews were conducted with government officials from two Mpumalanga departments and with experts in information security and ISP compliance to refine and validate the framework. The outcome of the interviews with government officials and experts resulted in the inclusion of additional components and factors to the conceptual ECISP framework. The outcome of this process is a validated framework intended to enhance end-user compliance with ISPs within government institutions. The main aim of the research study is to integrate literature findings with practical insights from government officials and experts to develop, refine, and validate a framework for end-user compliance with ISPs in Government departments (the ECISP Framework). The ECISP Framework provides guidance that enables organisations to address specific challenges related to end-user compliance with ISPs, thereby improving the likelihood of achieving end-user compliance with ISPs. 2026-01-01T00:00:00Z https://ir.unisa.ac.za/handle/10500/32778 Multi-lingual emotion detection in speech using hybrid neural network architectures Norval, Michael John The advancement of Deep Learning has revolutionised numerous fields, including humancomputer interaction (HCI) and emotion detection. In speech recognition, voice assistants such as Apple's Siri, Microsoft's Cortana, and Google's Assistant have become integral to everyday technology use. However, current emotion detection systems often need more support for African languages, presenting a significant research gap. The aim and objectives of the study are to develop a Speech Emotion Recognition (SER) system tailored to South African languages, with a specific focus on Afrikaans. The primary objective is to compile an Afrikaans speech corpus for emotion recognition. Furthermore, it explores hybrid neural network architectures combining CNN, Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks for improved accuracy. Finally, the aim is to investigate the optimisation of SER models for multilingual cross-language support. This study adopts a pragmatic research paradigm, utilising the Design Science Research Framework (DSR). Data is collected quantitatively. The literature review covers human speech, speech physiology, neural network architectures, speech corpus, and data extraction methods. Hybrid architectures and preprocessing techniques are examined to identify the most effective configurations. The Afrikaans speech corpus is sourced from Creative Commons (CC) sources, with data augmentation using Generative Adversarial Networks (GANs). The study successfully compiled an Afrikaans speech corpus. Synthetic speech samples were created using frameworks such as Tacotron 2 and the WaveNet vocoder, thereby enhancing sound quality. For the Hybrid Neural Network Architectures: The novel Dendritic Convolutional Long Short-Term Memory (DCLSTM) architecture outperformed traditional CNN and LSTM models, effectively capturing nuanced and long-term emotional dependencies in audio data. The second Kalman filter variant was identified as the optimal preprocessing technique for noisy speech signals. A final accuracy of 78.50% was achieved, improving on baseline CNN and LSTM models. For the Multilingual SER System, the DenCaps model achieved up to 71.91% accuracy on multilingual datasets, demonstrating improved generalisation and better capture of temporal dynamics and spatial hierarchies in emotional speech. The findings highlight the effectiveness of hybrid neural network architectures and advanced preprocessing techniques in improving SER accuracy. The development of the Afrikaans speech corpus and the introduction of the DenCaps model represent significant advancements in emotion detection. These results underscore the potential for creating robust, multilingual SER systems that cater to diverse linguistic needs. The research objectives were successfully achieved through the development of a specialised Afrikaans speech corpus, the exploration of innovative neural network architectures, and the optimisation of models for multilingual support. The academic contributions pave the way for more accurate, contextually aware human-computer interaction systems that address the research gap in emotion detection for African languages. Text and abstract in English 2026-07-01T00:00:00Z https://ir.unisa.ac.za/handle/10500/32777 A quantified self technology framework for monitoring and managing diabetes Mutunhu, Belinda Background: Diabetes is a chronic disease that requires constant monitoring of blood glucose levels. However, in developing nations, most patients with diabetes fail to manage this disease. This is chiefly because the prevailing traditional methods involve infrequent hospital visits and the use of glucometers, spreadsheets, and logbooks. This is a tedious and inaccurate process that lacks real-time decision-making. Thus, most patients fail to be actively involved in the management of the disease, leading to serious complications. Quantified self-technology (QST), which uses wearable devices and mobile applications to monitor physiological parameters, offers an interactive, integrated solution for managing diabetes. However, owing to a lack of expertise, sociocultural challenges, and insufficient tailored theoretical frameworks, the adoption of QST is minimal in developing nations. Objective: The main objective of this study was to propose a framework for adopting quantified self-technology for managing diabetes in developing countries. Methodology: To understand QST, a systematic literature review identified the components of the QST framework. Resultantly, a conceptual framework was developed, which served as a guide in designing the research instruments for data collection. Thus, this study employed a single, embedded case study design and face-to-face interviews for data gathering. An interpretivist paradigm was used to interview 35 patients with diabetes from a hospital in Bulawayo, who were purposively and self-selected. Braun and Clarke's thematic analysis, facilitated by ATLAS.ti software, was used to improve methodological rigour. Results: The six core drivers and inhibitors of QST adoption among diabetics were identified as user characteristics, technology preparedness, perceived benefits, usability, social norms, and cybersecurity risks. Key user characteristics include digital literacy, self-efficacy, and awareness. Technological preparedness depends on cost, access to resources, training, and lowering technology anxieties. Over 70% of participants had a tertiary education, yet 69% were unaware of QST before the study. Participants perceived usability to be determined by usefulness, ease of use, and enjoyment. Furthermore, social norms can encourage or discourage adoption, depending on the views of the family and social surroundings. Cybersecurity risks may include privacy breaches, financial losses, and the misuse of information. Conclusion: This study proposes a framework for adopting QST to enhance diabetes self-care in developing countries. With the help of QST, patients can prevent acute complications, reduce hospital admissions, and be proactive in managing diabetes. The cybersecurity risks identified must be addressed to build trust and unlock QST benefits, including improved monitoring, adherence, motivation, data sharing, and cost savings. Thus, national health strategies should integrate QST into preventive care policies, as it will aid in monitoring, patient empowerment, and personalised treatment. However, the findings should be interpreted within the context of the study’s limitations, including self-selection bias, the single-hospital setting, and the focus on one QST application. Contributions: This research contributes to the field of health informatics theoretically. This is achieved through, first, offering a new conceptual framework for the adoption of QST within a developing country context and, secondly, identifying two new determinants, namely training and technology anxiety, and demonstrating the contextual operationalisation of digital literacy and motivation in a developing-country diabetes-management context. It also highlights the importance of explicitly recognising perceived benefits in adoption theory, demonstrating that understanding value is essential before introducing new technologies among patients. Practically, the framework provides actionable guidance for designing low-cost, user-friendly QST interventions suited to resource-constrained settings. Lastly, the framework is particularly valuable to healthcare providers, developers, and policymakers who seek to improve diabetes management through innovative self-monitoring solutions. Text and abstract in English 2026-02-01T00:00:00Z