A Model for Digitization Success in Ugandan TVETs: Evaluation Through Structured Walkthroughs and Simulation

This study proposes an information systems model to enhance the success of digitization projects in Ugandan Technical and Vocational Education and Training (TVET) institutions. The research was based on agency theory, with additional insights drawn from the DeLone and McLean Information Systems Success Model and the Dynamic Capabilities Framework. The model was developed based on key constructs such as Communication, Task Programmability, Goal Conflict, Shirking, and Process Quality. To evaluate its effectiveness, a structured walkthrough was conducted using a prototype simulator (SimPro), where expert evaluators assessed its usability, completeness, and performance. Results indicate that 96% of experts rated the model as highly usable, while 92% agreed that it accurately represents key digitization principles. The model’s usability significantly influenced expert recommendations for adoption (Spearman’s rho = 0.457, p = 0.001). Based on expert feedback, refinements were made to enhance stakeholder engagement, accountability tracking, and task efficiency. These findings suggest that the model has strong potential to improve digitization success rates by enhancing stakeholder engagement, accountability tracking, and task efficiency. Expert evaluators confirmed that these factors are critical to successful digitization in TVETs, indicating that structured implementation of this model could lead to more effective digitization outcomes. However, further empirical validation through real-world implementation is recommended to measure long-term impact

Forensic Investigation of Drug and Food Crimes in Digital Marketplace

This research holds great significance as it is anticipated to safeguard customers from harmful frug and food products in cyberspace and to enforce the law by offering solid evidence to bring criminals to justice. Furthermore, this research helps to better understand how crimes that take place in the marketplace are committed, which enables the implementation of more effective preventive measures. Besides, in order to combat cybercrime, the findings of this study may serve as the foundation for the creation of more effective digital forensic investigation techniques. In order to perform digital forensic investigations of drug and food offenses using the marketplace, this study aims to develop an efficient and successful model or implementation guideline. This seeks to methodically direct the inquiry process while adhering to relevant norms. The objective of this research endeavor is to provide a model or practical guideline that is both effective and efficient for using the marketplace to undertake digital forensic investigations of drug and food crimes. This seeks to methodically direct the research process while adhering to relevant criteria. The following stages make up the Design Science Research (DSR) method of research: The issue in this project is "How to conduct a digital forensic investigation for Drug and Food crimes using the marketplace so that it can be used as evidence in court?" Then, in order to adopt answers from related research and make adjustments linked to research difficulties, a literature review is conducted to locate prior research. A model or implementation guideline for performing digital forensic investigations of the marketplace is the type of solution or artifact anticipated in this project. The next phase is solution design, which involves using an existing forensic investigation framework to create an artifact design. Following every step of the framework, case study experiments are then conducted to test the artifact design. The examination of the artifact design by both experts and consumers is the last phase

Digitalization of Tobacco Taxation in Bangladesh: Reducing Evasion and Enhancing Public Health

This study investigates the worldwide practice of digital tax systems to develop a digital taxation model for Bangladesh and identifies the prospects and challenges of implementing this model to address tax evasion. This study applies a qualitative approach to research. 30 in-depth interviews (IDI) with retailers of tobacco products and 10 key informant interviews (KII) with National Board of Revenue (NBR) officials, Tobacco control activists, and tax experts have been conducted using semi-structured interview guidelines. Secondary data has been collected from various reports, and journal articles. Data has been analyzed using the thematic analysis technique. The potential benefits of implementing a digitalized tax system encompass minimization of tax evasion, better monitoring, tracking, and tracing systems, transparent tax administration, and developing an efficient tax collection system. Despite the many advantages of digital tax systems, several challenges must be addressed. These include administrative resistance due to a lack of skilled manpower and modern infrastructure and the difficulties associated with registering tobacco companies and farmers. The implementation of proposed digital taxation model is expected to control tobacco tax evasion which ultimately increases prices and contributes to the overall goal of reducing tobacco consumption and enhance public health in Bangladesh

Improving Dolan Banyumas App: A Design Thinking Approach to Enhance Tourism Services

The Dolan Banyumas application is a digital step to support tourism in Banyumas Regency. However, the results of observations and evaluations conducted show that the design of the user interface (UI) and user experience (UX) of this application is still less than optimal, with incomplete information, confusing navigation, and unattractive application design. This study aims to redesign the application using the Design Thinking approach, which consists of five stages: empathize, define, ideate, prototype, and test stages. Usability was assessed using the System Usability Scale (SUS) with a 10-question Likert scale survey distributed to 30 respondents. Evaluation results using the System Usability Scale (SUS) method showed an increase in the average score from 63 to 81.42, which classifies the app into the “Good” and “Acceptable” categories. Improvements include easier-to-use navigation, more complete tourist information, and the addition of new features such as ticket booking and bus tour maps. The user satisfaction rate increased from 60% to 87%, while efficiency rose by 30%. Based on Net Promoter Score (NPS), the app is categorized as “Promoter.” The Design Thinking approach proved effective in improving the quality of user experience

Gender Motivations for TikTok Content Creation: A Comparative Study of Male and Female Students’ University Students

TikTok has rapidly become a leading platform for youth self-expression and digital participation, yet little is known about how gender influences motivations for content creation. This study explores gender-based differences among South African university students, addressing a key gap in the literature. Using a mixed-methods design, 100 students (50 male, 50 female) were surveyed and focus groups with 25 participants were conducted to examine content types and motivational drivers, over a three-month period. Findings reveal clear distinctions: female students are primarily motivated by self-expression, social connection, and stress relief, favoring fashion, lifestyle, and dance content. Male students, by contrast, focus on entertainment, follower growth, and raising awareness, often creating comedy and educational videos. These patterns reflect broader social norms and platform dynamics, emphasizing different gratifications by gender. The study is guided by the Uses and Gratifications Theory in understanding these gendered motivations is essential for fostering more inclusive and responsive social media environments. It offers practical insights for educators designing digital literacy interventions, as well as for platform developers aiming to enhance inclusivity and user engagement

Predicting Respiratory Conditions Using Random Forest and XGBoost

This study examines the performance of Random Forest and XGBoost in predicting the diagnosis and severity of respiratory diseases using a simulated dataset of 2,000 patient records. The models were tested on two classification tasks: identifying disease types (e.g., pneumonia, influenza) and classifying severity levels (mild, moderate, severe). Both models achieved perfect accuracy in severity classification, with 1.0000 ± 0.0000 cross-validation scores, demonstrating strong stability under balanced class distributions. However, in the diagnosis task, Random Forest underperformed on minority classes, particularly pneumonia, with a recall of 0.18 and F1-score of 0.31. XGBoost, on the other hand, achieved superior results across all classes, including minority cases, with 0.9825 ± 0.0170 cross-validation accuracy and perfect test set performance. These findings highlight XGBoost’s robustness in handling imbalanced and multiclass medical data, making it a promising candidate for clinical decision support. Future work should address class imbalance and explore explainability techniques to improve trust and transparency in real-world applications

Technology Acceptance Model TAM using Partial Least Squares Structural Equation Modeling PLS- SEM

The rapid advancement of digital technologies necessitates a deeper focus on user acceptance and satisfaction, particularly within the framework of the Technology Acceptance Model (TAM), analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). This systematic literature review examines 36 articles published between 2020 and 2025, revealing that factors such as trust, system quality, perceived enjoyment, service quality, and technological self-efficacy significantly influence user satisfaction. These external variables enhance the explanatory power of TAM, providing a richer understanding of user interactions with digital platforms such as e-commerce, e-learning, and mobile banking. PLS-SEM's ability to manage model complexity, non-normal data distributions, and interrelated constructs further validates its suitability for this research. The findings suggest that integrating these external factors improves both the theoretical and practical aspects of TAM in the context of technology adoption. Future research could explore additional industry-specific applications for emerging technologies

Data Warehousing for Optimizing Healthcare Resource Allocation in Botswana

Healthcare resource allocation remains a persistent challenge in Botswana, primarily due to inefficiencies in data management that obstruct equitable distribution and evidence-based decision-making. Traditional allocation approaches in Botswana exhibit severe fragmentation, low interoperability, and an absence of real-time data analytics factors that contribute to service delivery disparities, especially in rural and underserved areas. In contrast, developed countries have leveraged data warehousing to optimize healthcare resource planning, offering Botswana a proven yet untapped strategic opportunity. This study designs and validates a context-sensitive data warehouse methodology, applying the Kimball Lifecycle model as the guiding framework. A mixed-methods design was adopted, incorporating qualitative interviews with 24 healthcare practitioners and administrators across public and private health facilities, along with quantitative surveys assessing the state of 12 existing health data systems. Results reveal systemic shortcomings in data accuracy (average error rates of 22%), timeliness (with a median data update lag of 14 days), and accessibility (only 38% of facilities had centralized access). Post-implementation of the prototype data warehouse, significant improvements were noted: data accuracy increased by 47%, data accessibility across departments rose to 85%, and decision turnaround time was reduced by 33%. The warehousing also demonstrated cost-effectiveness, reducing redundant data handling expenses by an estimated 18% over six months. In conclusion, this study presents a robust, scalable, and locally adaptable data warehousing framework that effectively addresses Botswana’s systemic challenges in healthcare resource allocation

A Systematic Literature Review on Machine Learning Algorithms for the Detection of Social Media Fake News in Africa

Fake news has been around in history before social media emerged. Social media platforms enable the creation, processing, and sharing of various kinds of content and information on the Internet. While the mediums of information and content shared across social media platforms are hard for users to authenticate, if users are tracking fake information or fake content, it can harm individuals, society, or the world. Fake news is increasingly becoming a worrisome issue, especially in Africa, because it's difficult to identify and stop the distribution of fake news. Due to languages and diversity, it is difficult for humans to understand and subsequently identify fake news on social media platforms, so high-level technological strategies, such as machine learning (ML), would be able to tell if the content is false material. As such, this study sought to identify effective ML classifiers to detect fake news on social media platforms, and the systematic literature review followed the PRISMA standard. The study identified 14 effective ML classifiers to manage fake news on social media platforms, including Random Forest, Naive Bayes, and others. Four research questions guided the study focused on the effectiveness of the classifiers, their applicability for detecting different forms of false news, the features of the dataset size and features, and the metrics that were created to assess the metrics. A conceptual framework known as the Information Behavioral Driven Social Cognitive Model (IBDSCM) was proposed in a bid to affect the fake news detection on social media platforms. Overall, this study establishes a contribution to understanding the ML algorithms for detecting false news in Africa and allows for a conceptual base for future studies

Impact Assessment of Digital Learning Tools in South African Higher Education

Technological advancements have significantly reshaped the operational landscape of tertiary institutions, enhancing both student and academic efficiency processes. In South Africa, many students in higher learning institutions scrambled to use technology for teaching and learning due to load shedding, poor internet connectivity, lack of technological skills, lack of technology training by the tertiary institutions, etc. This study employs the UTAUT to understand better how technological innovations impact South African higher institutions. The UTAUT model includes components such as effort expectancy, self-awareness, social influence, facilitating conditions, and voluntary use to fully understand the factors influencing technology development and adoption. Three hundred and ten (N=310) students from underprivileged tertiary institutions in the Eastern Cape participated in this study.  The study used a quantitative research methodology based on a 5-point Likert scale to gauge the respondents' intention to use technology for teaching and learning. Regression analysis and NOVA statistical tools were used to analyse the acquired data. The findings revealed that most participating students believe that technological advancements had a positive impact on their ability to teach and learn. The research findings imply that faculty should implement training programs on digital tools, improve IT infrastructure, provision of free internet bundles, and develop policies that support the adoption of e-learning technologies