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In South Africa, only 6% of the population is expected to retire with financial independence, and the implementation of the Two-Pot retirement system in September 2024 exacerbates the retirement savings dilemma, underscoring an urgent necessity for creative retirement planning solutions. This article thoroughly delineates the present status of research into the utilisation of Robo-advisors in retirement planning. This analysis evaluates the appropriateness and feasibility of employing Robo-advisors to enhance and democratise digital financial advice within the context of a developing economy like South Africa, particularly through the adoption of the Two-Pot retirement scheme. A comprehensive literature analysis was conducted utilising the Emerald Insight, ProQuest, and Scopus databases to find peer-reviewed journal articles published between 2019 and 2024. These articles critically assessed the function of Robo-advisors in democratising financial advice, providing a cost-efficient, accessible, and perhaps less biassed alternative to conventional advising services. The study highlights the imperative for future research to guarantee the ethical, transparent, and controlled implementation of robo-advisory services in South Africa. The literature analysis delineates five pertinent study themes: emerging literature on Robo-advisors, behavioural dimensions, risk assessment, regulatory frameworks, and Robo-advisor characteristics pertinent to the South African retirement context, considering the Two-Pot retirement system.The report delineates potential research opportunities for each highlighted issue to aid scholars in performing empirical investigations aimed at optimising retirement planning in South Africa through the use of Robo-advisors.

This research assesses the levels of digital financial literacy (DFL) and the patterns of Fintech adoption among university students in South Africa. It expands the Unified Theory of Acceptance and Use of Technology (UTAUT) framework to investigate the impact of demographic factors on DFL as a precursor to Fintech utilisation. A quantitative methodology utilised convenience sampling to distribute 376 questionnaires at two public universities in South Africa. Data analysis employed descriptive statistics, exploratory factor analysis, Kruskal-Wallis tests, and multiple linear regression using IBM SPSS version 29.0, with reliability evaluated through Cronbach's alpha coefficients. Findings reveal moderate to high levels of DFL among university students; however, notable knowledge deficiencies exist concerning consumer rights and redress mechanisms. Demographic analysis indicated that gender had no significant impact on DFL levels, whereas income, age, and educational attainment showed significant correlations. Three components of DFL demonstrated positive and significant correlations with university students' intentions to adopt Fintech products and services; however, awareness of digital financial risks did not significantly influence these adoption intentions. The findings enhance the theoretical framework of technology acceptance models in emerging markets and offer empirical evidence regarding DFL's influence on Fintech adoption. This study provides evidence-based recommendations for policymakers, financial institutions, and educational administrators aiming to improve financial inclusion initiatives and increase Fintech utilisation among university students in South Africa.

This study explores the factors influencing youth entrepreneurs in Tanzania to adopt crowdlending as an alternative source of finance and the challenges they encounter in the process. Using a qualitative case study approach, interviews were conducted with 15 young entrepreneurs who successfully accessed loans through a crowdlending platform. Thematic analysis was conducted through transcription, coding, and theme development processes. The findings indicate that financial necessity and limited access to traditional banking were the primary motivators for adopting crowdlending. However, the study also found that entrepreneurs often have a limited understanding of the crowdfunding process, as many access the platform through intermediaries. Additionally, the study identifies high interest rates and transaction costs imposed by financial intermediaries as major barriers to the perceived benefits of crowdlending. The findings underscore the importance of tailored financial literacy initiatives, user-centred platform design, and policy frameworks to unlock crowdlending’s potential for inclusive youth entrepreneurship in Tanzania. 

This study investigates the role of large language models (LLMs) in retail investors (RIs) decision-making processes from the perspective of the Theory of Planned Behaviour (TPB). It explores whether LLMs can replace or change the role of financial experts and whether introducing LLMs may lead to more infromed RIs’ decisions. Qualitative interviews were conducted with experienced RIs (n = 8). Secondary data were gathered from YouTube recordings (n = 44). Thematic analysis and Retrieval-Augmented Generation (RAG) methodology was used for data extraction and analysis of the scripts. The findings indicate that while LLMs have the potential to enhance accessibility to expert opinions and provide more informed investment decisions, they are unlikely to replace human experts. RIs show a preference for combining LLM insights with human expertise, recognising the limitations of LLMs in managing complex and nuanced investment information. The study highlights the usefulness of the TPB as a framework for the exploration of the topic. It also introduces a novel research method - advanced data extraction techniques on a vast unstructured dataset.?Results contribute to the understanding of LLMs potential in supporting RIs and confirms the usefulness on the AESTIMA tool for data extraction and analysis.