This paper investigates the benefits and challenges of integrating artificial intelligence (AI) and data analytics in South African digital banking, addressing the research question: What are the benefits and challenges of using AI and data analytics in digital banking in South Africa? Guided by an interpretive qualitative design, the study used semi-structured online interviews with 11 professionals drawn from banks, fintech firms and regulatory institutions, and applied thematic and content analysis to the resulting data. The findings show that AI and data analytics enhance customer experience, improve operational efficiency and support informed decision-making, while key challenges include data privacy and security concerns, high implementation costs, resistance to technological change and legacy-system constraints. A major policy implication is the need for robust regulatory and governance frameworks, combined with targeted skills development and infrastructure investment, to enable ethical and sustainable AI deployment in digital banking. The study offers an original contribution by providing context-specific empirical evidence from an emerging market, extending a literature base that has largely focused on developed economies and informing both policymakers and practitioners on how to optimize AI and data analytics in South African digital banking.

Several studies have examined how algorithmic systems shape visibility, particularly in how non-Western regions are represented through inherited biases and colonial narratives. Moving beyond a focus on algorithms alone, especially in the African context, this study examines travel-related content on TikTok to understand how platform curation and user engagement work to shape representation. Using content analysis, the study shows that TikTok frequently foregrounds wildlife and nature-focused portrayals of Africa, reinforcing familiar narratives. It also shows that these portrayals are sustained through feedback loops, where users engage with and create content that aligns with what the platform already promotes, further informing algorithmic recommendations. These findings point to the importance of algorithmic transparency, digital literacy, and more careful attention to representation in shaping how Africa is encountered on digital platforms.

Blockchain technology has the potential to eradicate fraud, improve institutional legitimacy, and increase efficiency, security, and transparency. Despite its potential, the adoption of blockchain technology among staff in higher education institutions (HEIs) remains limited, particularly in emerging economies like Oman, due to limited technological infrastructure, insufficient preparedness of institutions, and a lack of skilled employees. Research undertaken in developed countries has identified the advantages of blockchain for credential verification and administrative purposes, but these findings cannot be generalized in Oman due to its distinct context. To fill this research gap, the objective of this study is to identify, explore, and prioritize the critical factors that influence staff adoption of blockchain technology in HEIs in Oman. This study employed the Analytic Hierarchy Process (AHP) to prioritize the factors, and data were collected from 16 staff members employed at universities in Oman. The findings identified Leadership and Governance as the most important factors, highlighting the need for strong top management support, policies, and strategic direction. The second-most important factor, financial management, emphasizes the importance of financial investment, cost-benefit analysis, and financial audit. Technical Infrastructure ranked third, highlighting scalability and security, data security and privacy, and system interoperability. Collaboration and Partnerships ranked fourth, emphasizing collaboration and partnerships with academia, industry, and government, while Human Resources ranked lowest, indicating the need for skilled workforce training and capacity building to effectively adopt blockchain technology. This study offers insights for policymakers, HEIs, administrators, staff, and blockchain technology developers. The originality of this study lies in bridging the gap between academic theory and institutional decision-making by providing the policy-relevant prioritization model for blockchain technology adoption among staff in HEIs, grounded in the Oman context and validated through expert consensus.

The paper presents ORIENT-Net as a tool that processes texts in terms of their orientalist views. This is done by the detection and classification of such texts on social media Twitter. An Orientalist view of the East can see the cultures there as being quaint, obsolete, or entirely unlike the West, which Westerners can be very often influenced by. The demographic figures are also computed for the ORIENT-Net project, to be able to provide the necessary NLP, ML, and data mining solutions that are tailored to the specific demographics. The framework collects the relevant tweets on the basis of keywords that also cover a range of different kinds of Orientalist statements. The very subtle orientalist markers are identified through three linguistic features: TF-IDF, n-grams, and BERT embeddings that are the result of fine-tuning. The CatBoost classifier is the one that does the classification of the tweets into ‘orientalist’ and ‘non-orientalist’ categories, and the system’s performance is measured through accuracy evaluation, along with precision and F1-score assessments. The model relies on SHAP and LIME for interpretability and decision explanation. The framework makes online detection of oriental discourse automatic. The results indicate the high accuracy and clarity of orientalist identification. This system offers a scalable tool to support sociocultural research and inform evidence-based policy decisions. The uniqueness of this research is that it combines postcolonial theory and computational modelling via the ORIENT-Net framework, thus providing an interpretable and scalable method for online ideological discourse analysis. Future work will focus on expanding the dataset across multiple languages and platforms, as well as refining detection capabilities to capture more nuanced and context-dependent discourse patterns.

To explore in depth the impact of digital inclusive finance (DIF) on rural revitalization, this study employs the entropy method to measure the level of rural revitalization across 30 provincial-level administrative regions in mainland China from 2014 to 2023. It further conducts an empirical test on the relationship between the two variables and the mediating paths using the two-way fixed effects model and the mediating effect model. The study finds that DIF directly improves rural revitalization by 6.5%. Mediation tests reveal digital infrastructure (22.2% of total effect) lowers transaction costs, while education (24.8%) enhances financial literacy. Heterogeneity analysis shows coverage breadth (5.2%) contributes most among DIF sub-indices, followed by usage depth (2.6%) and digitization (2.2%). At the practical level, this study provides operational guidance for local governments and financial institutions, such as prioritizing the establishment of village-level service stations and optimizing regional resource allocation. It also offers evidence for policymakers to improve assessment indicators and develop differentiated policy frameworks, with its findings holding reference value for developing countries worldwide.

Real-time stereo image retargeting for augmented reality (AR) and virtual reality (VR) necessitates precise per-pixel depth estimation and ultra-low latency performance on resource-limited edge devices. Current disparity convolutional neural networks (CNNs) and retargeting pipelines are unable to meet these demanding requirements concurrently. This paper presents EASNet, a compact end-to-end framework that unifies geometry-aware proposal generation, parallax-aligned feature encoding, sparse candidate aggregation, and uncertainty-guided refinement to enable high-fidelity stereo retargeting on edge architectures. This system enhances stereo vision through Epipolar-Adaptive Disparity Proposals (EADP) for search space reduction, a Parallax-Directed Deformable Encoder (PaDDE) for improved matching in repetitive and low-texture areas, Sparse Epipolar Candidate Volume (SECV) with Edge-Consistent Routing (ECR) for efficient, boundary-preserving cost aggregation, and Lightweight Uncertainty-Guided Refinement (LUGR) for sub-pixel structure and occlusion correction. Evaluated on high-resolution indoor stereo data, EASNet attains a favourable trade-off between accuracy and efficiency (≈0.21 M parameters, 3.42 GFLOPs) while improving disparity fidelity and visual coherence required for retargeting (reported EPE ≈ 1.78 px, D1 ≈ 5.02%, VC ≈ 93.7%). The design emphasizes quantization compatibility and deterministic latency, enabling practical deployment on AR/VR edge devices. We analyze ablations, per-scene behaviour, and k-fold stability, discussing limitations like indoor bias, extreme occlusion, large baselines, and future EASNet extensions.

The manual peeling of cassava is laborious, prone to injuries, and limits processing speed, contributing to postharvest losses due to cassava’s highly perishable nature. To address this challenge, this study aims to evaluate the financial and economic viability of installing cassava peeling machines and the key constraints to their adoption among small-scale processors. Primary data were collected from a random sample of 300 cassava processors, and the analysis employed both discounted and undiscounted measures of project worth, the Garrett Ranking Technique, and Kendall’s Coefficient of Concordance. The results show that installation of the cassava peeling machine is financially viable, with a Net Present Value (NPV) of GH₵229,870.00, a Benefit–Cost Ratio (BCR) of 1.61, an Internal Rate of Return (IRR) of 27%, and a payback period of 4.3 years. Similarly, economic viability was confirmed with an NPV of GH₵132,950.00, a BCR of 1.4, an IRR of 26%, and a payback period of 3.8 years. High initial investment cost emerged as the major constraint. Policy interventions should therefore prioritize reducing entry costs through public-private partnerships. This study contributes by bridging engineering innovation with economic adoption analysis, offering empirical evidence to guide technology diffusion in rural agro-processing.

This study investigates the role of innovative financial performance metrics in promoting the sustainable development of small and medium-sized enterprises (SMEs) in China. Responding to the limitations of single-indicator financial assessments, the research develops a composite Financial Performance Evaluation (FPE) index using factor analysis on ten key financial indicators across profitability, solvency, operational efficiency, and growth dimensions. Drawing on panel data from 82 listed SMEs over the period 2017–2022, the study applies a random-effects regression model to examine the impact of FPE on firm profitability, proxied by return on net profit (RON). The findings reveal a significant positive relationship between FPE and SME development, underscoring the strategic value of multidimensional financial evaluation in enhancing enterprise growth. The results are robust to alternative model specifications and endogeneity checks. This study advances financial diagnostics and offers actionable insights for policymakers, financial institutions, and SME managers. These insights can strengthen performance metrics to foster more inclusive and resilient enterprise development in emerging economies.

A significant change in the financial industry has been brought about by technological advancements and shifting consumer behaviour. The result has been the emergence of fintech. Consequently, this paper constructs predictive models to forecast the adoption of fintech in South Africa. It investigates the predictive strengths of remittances, borrowed funds, age, access to finance, and digital usage on fintech adoption. Using data sourced from Findex and the South African Reserve Bank (SARB), this paper uses machine learning (ML) approaches namely, multi-linear regression and the ElasticNet algorithms to carry out its predictions. The results offer evidence that the selected factors are key drivers of fintech adoption in South Africa. A good fit of the model is checked through the train and test set score while the error is checked through the root mean square error. The prediction capability of the model is reported at 0.99 with a cross validation of 0.97 thus implying that the chosen inputs provide a good explanation for fintech adoption in SA. Fintech firms are therefore urged to incorporate these findings in formulating strategies to attract and retain customers. The findings also contribute towards the operational strategies that can be adopted by fintech firms.

This study examines the impact of regional institutional quality (level two) on firm-level innovation (level one) in Sub-Saharan Africa (SSA), where cultural and institutional diversity challenges frameworks derived from homogeneous contexts. Analyzing 7,578 firms across Ethiopia, Kenya, Rwanda, Tanzania, Uganda, Zambia, and Zimbabwe (2011–2019 World Bank Enterprise Survey data), the paper highlights the need for multi-level analysis to capture regional variations. The study challenges uniform institutional frameworks, emphasizing SSA's diversity in religion, ethnicity, and language. Using hierarchical logistic regression, it reveals direct firm-level effects of internal resources on innovation and cross-level influences of regional institutions. A composite measure of regional institutional quality significantly predicts innovation, with regulatory quality showing notable moderating effects. Contributing to innovation dynamics in diverse settings, the paper advocates for multi-level approaches to understand firm behavior in heterogeneous environments. Its originality lies in analyzing firm innovation while preserving variations in internal resources and macroeconomic structures, addressing limitations of single-level analyses in prior research.