The Independent Institute of Education

This study explored the relationship between consumerism, corporate social responsibility (CSR), and ethical performance in Zimbabwe through a survey analysis. The research explored the differences in consumerism between developed and less developed countries and identified various influence consumerism, including economic situation, level of consumer education and awareness, and cultural considerations. The concept of consumerism was familiar to Zimbabwean consumers, but they lacked a comprehensive understanding of how it could be used to hold businesses accountable. Consumerism is in its infancy in Zimbabwe, and citizens lack awareness of their rights and obligations. This research relied on the understanding and perceptions of consumers to examine the relationship between consumerism, CSR, and ethical performance in Zimbabwe, as well as the effects of consumer activism on corporate behaviour. The data for this exploratory study was collected and analysed using qualitative methodologies from a representative sample of 200 Zimbabwean consumers chosen at random. The collected questionnaires were carefully analysed and structured to identify themes and key differences, providing a holistic understanding of the study. The findings indicated that consumers in Zimbabwe prioritise price, brand, quality, and country of origin over CSR and the ethical performance of organisations. This suggests that businesses in Zimbabwe have little incentive to prioritise CSR and ethical behaviour unless it aligns with the demands of consumers. The research highlighted the need for more education and awareness-raising efforts around CSR and ethical business practices in Zimbabwe. Overall, the study underscored the importance of consumerism and its impact on corporate social responsibility and ethical performance in Zimbabwe.

African traditional healing practices are integral to the cultural and spiritual identity of many communities in South Africa; however, their perceived role on mental well-being has not been thoroughly investigated. In Gqeberha, South Africa, where mental health challenges are prevalent, traditional healers provide culturally relevant and accessible alternatives to mainstream mental healthcare. Despite their importance, there remains a significant gap in understanding how traditional healers contribute to mental healthcare and how their methods could complement modern psychological interventions. This study aimed to explore the perceptions, beliefs, and practices of African traditional healers regarding their role in promoting mental well-being among residents of Gqeberha. Adopting a qualitative methodology, the research employed semi-structured interviews with traditional healers who regularly engage with individuals experiencing mental distress. Using thematic analysis, the research captured in-depth perspectives and identified key themes, offering a comprehensive understanding of traditional healing practices within Gqeberha’s cultural context. The study highlights the potential for integrating traditional healing practices into a broader mental healthcare framework, emphasizing the importance of collaboration between traditional healers and mental health professionals. This approach can foster a more inclusive and culturally sensitive mental healthcare system, ultimately benefiting the mental well-being of not only Gqeberha residents but also diverse communities across South Africa.

BackgroundPulmonary tuberculosis (PTB), remains one of the leading causes of death from infectious diseases worldwide, its burden is disproportionately affecting low- and middle-income countries (LMICs), including Tanzania. This study aimed to examine the direct and indirect costs incurred by patients and their families prior to PTB diagnosis, and how such costs influence patients’ health-seeking behaviours.MethodsA mixed-methods approach was employed, integrating qualitative and quantitative methods for data collection, analysis, and interpretation. The study was part of a longitudinal observational cohort that included 261 adults diagnosed with PTB. Quantitative data were collected using a structured questionnaire adapted from the WHO’s generic patient cost survey tool, administered after TB diagnosis was confirmed. To gain deeper insights, additional data were gathered through, in-depth interviews (IDIs) with purposively selected participants, aimed at identifying both financial and non-financial challenges experienced by patients and their family supporters before to diagnosis.ResultsOf the 261 respondents, 59% were men, 48% were married, and 51% were living with HIV, with a median age of 35 years. The majority 185(72.8%) delaying visiting a healthcare facility (HCF) by more than four weeks following the onset of symptoms. The average total pre-treatment cost per patient was USD 28.8, comprising direct medical, direct non-medical, and indirect costs. These costs varied based on the number and type of healthcare providers consulted. Qualitative findings indicated that participants frequently associated this pre-treatment costs with delays in seeking formal care, often influenced by self-medication, limited awareness of TB symptoms, and long distances to formal HCFs.ConclusionThis study highlights the significant financial and non-financial barriers faces by patients diagnosed with PTB and their families before diagnosis. Delays in care-seeking not only worsen health outcomes but also increase the overall economic burden. Addressing these challenges requires a comprehensive, multisectoral approach to improve early diagnosis, reduce patients costs and strengthen TB control efforts in high-burden settings.

Despite the increasing popularity of Valorant in esports, there is limited research done on how team compositions affect match results when considering various maps and changing game metas. To address this gap, the study aims to identify the optimal team compositions by investigating the match data from the Valorant Champions Tour between 2021 and 2024. It was observed that the teams lack data driven strategies which adapt to agent role changes, map dynamics and meta changes. The study uses quantitative strategies to analyse role distributions and real game performance of Duelists, Controllers, Initiators and Sentinels combinations. The results show that teams that used Duelists with Controllers, Sentinels and Initiators won more often than teams that used only Duelists in 2024. This demonstrates that competitive teams are changing their priorities. The study shows that effective agent roles depend on the map. Duelists are better on Bind and Controllers perform better on Ascent. The findings indicate that Controllers and Initiators were the most popular choices in 2024. The research adds to the knowledge about rolebased success and changing team dynamics in competitive Valorant and it also fills a gap in data driven esports research on strategy and team composition.

The transportation sector significantly influences Greenhouse gas emissions, underscoring the necessity of creating effective and sustainable transportation systems. Intelligent charging and control strategies are essential for vehicles’ hybrid battery energy storage systems (HBESSs) to lower emissions and increase energy efficiency. There is a dearth of studies on HBESSs and the intelligent charging and control strategies created especially for them, even though prior work has concentrated chiefly on employing smart grids to manage energy supply and demand within transportation systems. Therefore, more studies are necessary to develop these intelligent charging and control methods for transport vehicles’ HBESS, especially in settings with smart grid connectivity. Transportation systems might significantly reduce emissions and increase their energy efficiency by using HBESSs, which usually incorporate high-power and high-energy density storage, as well as sophisticated charging and control strategies. This study uses smart-grid-enabled systems to present a novel multi-objective predictive control (MOPC) paradigm for intelligent charging and management of hybrid battery-driven transport vehicle (TV) systems. To improve overall energy efficiency and lower TV emissions, the proposed MOPC technique seeks to optimise charging schedules, control energy flow between hybrid battery components, minimise charging costs, maximise the usage of renewable energy, and limit battery degradation.

The National Basketball Association provides strong potential for predictive analytics due to its global fan base, structured season and rich historical data. This research evaluates the ability of machine learning models such as logistic regression, support vector machines and random forests to predict the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{2 0 2 3 - 2 0 2 4}$</tex> NBA Championship winner. The research used 20 years of team performance statistics to select predictive features including field goals percentage, two-point percentage, three-point percentage, assists, rebounds and blocks through correlation-based feature selection. The models achieved evaluation through accuracy, precision, recall and F1 score measurements. Logistic regression produced the most balanced and interpretable results despite the class imbalance in the dataset because it correctly predicted Boston Celtics as the championship winner at 94.99 % probability. Random forest produced the highest accuracy rate of 93 %, yet it did not correctly predict the winner because it performed poorly on minority class detection. Support vector machine yielded intermediate performance. The success of logistic regression demonstrates its effectiveness for sports datasets with imbalanced outcomes and proves that conventional team statistics remain valuable for predictions. The research findings expand sports analytics knowledge while showing how machine learning systems can assist professional basketball teams with strategic planning. The developed framework can be adapted to support performance management and talent development in emerging basketball leagues. Future research could use advanced metrics together with neural networks and other alternate modelling techniques to boost predictive power and solve binary classification challenges.

This paper presents the co-design and deployment of a culturally contextualized mobile interface aimed at enhancing transparency in an AI-and IoT-enabled hydroponics system within the Indigenous San community in Donkerbos, Namibia. Building on a previously implemented AI-driven hydroponic system developed in South Africa, the project introduces a webbased application tailored to local needs through CommunityBased Co-Design (CBCD). The design process emphasized relational accountability, local symbolism, and transparency to foster user agency and system usability. By integrating real-time data visualization, explanatory feedback, and interactive AI assistants powered by Large Language Models (LLMs), the application enabled community members to better understand and manage the system. Our findings demonstrate that transparent AI interactions can improve user agency and promote technological acceptance. This work contributes to the field of Responsible AI by illustrating how co-design can bridge technological and cultural contexts to support sustainable agriculture and digital inclusion in marginalized rural communities.