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The rapid integration of Artificial Intelligence (AI) into higher education is fundamentally reshaping how students access and present knowledge. Within legal education particularly, AI tools increasingly enable students to generate summaries, conduct research, draft legal content and retrieve information with minimal engagement in traditional legal reasoning processes. At the same time, South African law faculties remain tasked with cultivating industry ready candidate legal practitioners who possess critical thinking abilities, ethical judgment, persuasive communication skills as well as strong legal research competencies. This creates an important pedagogical tension. While AI may enhance efficiency and technological fluency, its uncritical use may simultaneously undermine the intellectual rigour required for competent legal practice. This paper critically examines whether the growing integration of AI in legal education supports or compromises the cultivation of graduate attributes required within the South African legal profession. Using a doctrinal and conceptual methodology, the study analyses the graduate competency frameworks advanced by the Council on Higher Education and the South African Qualifications Authority. It further interrogates whether AI tools meaningfully contribute toward the development of legal reasoning, ethical sensitivity, drafting proficiency and contextual interpretation, all of which remain central to professional legal readiness. The paper argues that although AI presents significant opportunities for enhancing legal research efficiency and technological competence, overreliance on these tools risks producing graduates who are technologically capable yet jurisprudentially underdeveloped. It contends that AI cannot replace the human processes of critical engagement, ethical reflection and contextual legal interpretation that define legal education within a constitutional democracy. The study concludes that responsible AI integration in legal education requires careful pedagogical design and ethical oversight to ensure that innovation does not come at the expense of professional competence and intellectual integrity.

The study sought to relationship between enterprise risk management practices (ERM) and perceived sustainability of emerging and junior mining sector in South Africa as it creates employments and mineral diversification. This sector remains vulnerable to various risks factors including financial and regulatory among others.  The objective of the study was to examine the relationship between enterprise risk management practices and perceived sustainability of emerging and junior mining sector in South Africa. The study was anchored upon the governance theory. The study adopted the use of a quantitative methodology with a cross-sectional design using a secondary data panel downloaded from Kaggle data repository. The sample size of the study was 500 which included 262 males and 238 females who work in different mining organisational sizes. Regression analysis and structural equation modeling were tested and run to test the hypothesised statements. The results of OLS regression model 1 show that ERM_Governance has a positive and significant effect on sustainability and ERM_Value shows a negative and significant association, and other ERM dimensions are positive but not statistically significant. OLS regression Model 2 for perceived socio-economic importance shows that ERM_learning is a positive and significant predictor of socio-economic importance, and other ERM dimensions are not significant in this model. On the SEM front, ERM practices exhibited strong positive effects on both perceived sector sustainability and socio-economic importance (p < 0.001). Perceived sector sustainability significantly mediated the relationship between ERM practices and socio-economic importance (p < 0.001). Additionally, organisations size and mining experience were found to significantly moderate the ERM-sustainability relationship (p < 0.05), indicating the presence of contextual effects within the emerging and junior mining sector. The theoretical implication suggests that ERM should be taken as a monolithic construct but rather as a set of complementary dimensions with distinct outcome pathways. Practically, the results imply that policymakers and mining executives should prioritise strong governance and accountability systems to enhance sustainability perceptions while simultaneously investing in learning and collaborative capabilities to maximise socio-economic impact.

Cross-sector fraud between South Africa’s financial and telecommunications sectors has intensified, driven by sophisticated criminal networks that exploit operational disparities between these industries. Traditional fraud detection systems, which depend on centralised data sharing and siloed organisational structures, encounter substantial legal and practical obstacles under the Protection of Personal Information Act (POPIA). These include restrictions on cross-entity data transfer, data minimisation requirements, and transparency obligations for automated decision-making. This study developed and evaluated a privacy-preserving framework that integrates Federated Learning (FL) and Explainable Artificial Intelligence (XAI) to enable collaborative fraud detection while safeguarding customer privacy and ensuring regulatory compliance. A mixed-method approach was employed. Quantitative experiments were conducted on a dataset of 35,675 credit card transactions linked to telecommunications parameters, training a federated global model across simulated financial and telecommunications providers. A qualitative systematic literature review identified key barriers to cross-sector collaboration and distilled lessons from prior Federated Learning and Explainable Artificial Intelligence implementations. The federated model demonstrated strong detection performance, achieving a ROC-AUC of 0.98, near-perfect precision and recall for legitimate transactions, and an F1-score of 0.89 for the minority fraud class, all without exchanging raw customer data between institutions. SHAP analysis identified TransactionAmt and card1 as the most influential features, thereby satisfying POPIA's transparency mandates. While federated approaches did not consistently surpass siloed models in raw accuracy, this marginal trade-off reflects the essential privacy-utility equilibrium required for legal compliance. The framework effectively reconciled collective security with individual privacy, confirming that POPIA adherence need not diminish detection efficacy. This study provides a validated blueprint for financial and telecommunications providers in South Africa to collaboratively counter cross-sector fraud within regulatory constraints. The framework further supports audit trail generation and decision rationales, addressing compliance obligations. Drawing on systematic literature review insights, particularly regarding collaboration barriers and prior Federated Learning and Explainable Artificial Intelligence implementations, the findings offer regulators and investors practical guidance for meeting statutory requirements without compromising privacy rights.

The study examined the critical approach adopted by entrepreneurship and policy development in promoting socio-economic development. In this review. It also looked at the difficulties small, micro, and medium-sized businesses in South Africa face. The conceptual research looked at the influence of small, micro, and medium-sized companies (SMMEs) on South African development. The study's objective was to evaluate how small, medium-sized businesses (SMMEs) are now driving economic growth and solving social issues in a setting where big businesses and governments appear to be doing very little. As a result, the study examines how SMMEs might help the nation solve persistent societal evils, including poverty, inequality, and social isolation. A desktop literature review research design was adopted to collect data on the role of entrepreneurship in South Africa. The study further looks at policies and practices of big business and government that exacerbate social issues in communities and impede socio-economic development, as well as the specific roles that SMMEs play in achieving socio-economic development goals. The findings reveal that, among various developmental outcomes, SMMEs are at the forefront of generating employment and promoting social and political justice within the nation. Future empirical studies should account for and evaluate the importance of SMME contributions to constructing economic, social, and political justice in the nation.

Market participants usually seek exposure to global beta which are best achieved usually through large capitalization equities where higher idiosyncratic risk represents a potential source of alpha. This study empirically explored the persistence and drivers of idiosyncratic risk across the S&P 500, CAC 40, FTSE 100, DAX, and Nikkei 225 for a period of 20 years (2004 – 2024). Utilizing a quantitative risk decomposition framework, the analysis revealed a stark dichotomy where the S&P 500 exhibits minimal idiosyncratic risk, functioning as a proxy for global systematic factors. The Nikkei 225 was dominated by idiosyncratic risk which can be attributed to the unique domestic monetary policy. The European counterparts occupied a middle ground, with idiosyncratic risk amplified by sectoral concentrations. The correlation analysis further suggested a deep regional integration within the European markets and a diversification potential with the Japanese equity market. The findings challenged the notion that globalization has harmonized global financial market returns. These findings also demonstrates that idiosyncratic risk remains a significant and potentially compensable component of the concept of international investing, necessitating a strategic shift towards factor aware asset allocation.

Traditional asset pricing models assume rational investors and informational efficiency. However, persistent anomalies, and recurrent financial crises challenge these assumptions. This study synthesizes findings from leading international research to evaluate how cognitive biases shape asset prices and provides a comprehensive review and synthesis of behavioral asset pricing in international financial markets over a period from 2004 to 2024. Evidence from the extensive stock market data, cross-market behavioral heterogeneity and survey-based risk premia consistently demonstrates that behavioral effects are time-varying and regime-dependent. The findings indicate that behavioral heterogeneity intensifies during boom and bubble periods, diminishes during crashes, and generates predictable patterns in returns across asset classes and countries. Overall, the study re-iterates support for a dynamic behavioral asset pricing framework that integrates psychological factors into international financial market analysis.

The purpose of this study was to investigate the efficacy of hybrid forecasting models that integrate the classical Autoregressive integrated moving average framework, the support vector machines, the artificial neural networks, and random forest for predicting S&P 500 index returns. Utilizing daily share price data from 2015 to 2023, the methodology used a two-stage decomposition process where ARIMA captured linear dependencies, while the ML models projected nonlinear patterns within the residuals. The Out-of-sample analysis revealed that all the hybrid models significantly outperformed their standalone ARIMA benchmark across key accuracy metrics, such as mean absolute error, root mean squared error, and directional accuracy. The ARIMA-RF hybrid achieved the highest predictive accuracy and statistical significance, though it entailed a greater computational cost. The findings confirmed that the hybrid forecasting  capabilities to mitigate linear autocorrelation and ARCH effects, offering a robust approach for financial time series forecasting.

Employee attrition remains a persistent challenge for ICT organisations in Gauteng, South Africa, impacting performance, continuity, and workforce stability. High turnover disrupts project timelines, erodes institutional knowledge, and inflates recruitment and training costs. This study investigates attrition predictors by applying logistic regression to a synthetically generated structured dataset of 1,470 employee records in Gauteng. It examines whether workplace, demographic, and relational factors significantly influence turnover outcomes. Guided by three work-based practice frameworks: Social Exchange Theory, Herzberg’s Two-Factor Theory, and Job Embeddedness Theory, the research aims to identify meaningful retention drivers beyond surface-level attributes. A stratified random sampling method was used to construct a representative subset of 50 records, balancing class proportions within practical constraints. Pre-processing, encoding, and scaling were performed using Python libraries to ensure data integrity, followed by hypothesis testing and regression analysis to evaluate predictor significance. Findings show that relationship satisfaction and organisational commitment negatively correlate with attrition, reinforcing the importance of intrinsic motivators in retention. In contrast, gender, age, tenure, and income-related variables were statistically insignificant, challenging conventional HR assumptions. These results contribute to organisational behaviour literature by highlighting relational and psychological factors over demographic predictors. They suggest that fostering a supportive work environment and cultivating employee engagement may be more effective than relying solely on compensation or tenure-based incentives. The study recommends future research that incorporates larger samples from sectors beyond ICT, employs hybrid methodologies and longitudinal designs, and integrates qualitative insights from interviews or surveys to deepen understanding and inform proactive retention policies.

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.  

Long memory suggests that past returns can influence future returns over extended periods, indicating potential predictability. The aim of this study was to empirically examines the interplay between long memory effects, downside risk, and stock returns across five major international equity markets (S&P 500, FTSE 100, Nikkei 225, DAX, and ASX) from 2000 to 2022. An ARFIMA model which quantify fractional differencing was used in conjunction with a correlation matrix, regression and Granger causality test to investigate the interplay. The findings revealed a significant long memory across all markets, with Nikkei 225 exhibiting the strongest persistence, attributed to Japan’s prolonged economic stagnation. Counterintuitively, lagged downside risk positively predicts returns, aligning with behavioural theories of investor overreaction to tail events. Bidirectional causality between risk and returns underscores reflexive market dynamics. The study bridges a critical gap by integrating long memory into downside risk models, demonstrating that persistent volatility amplifies tail risk premiums. The findings of this study suggest that financial market regulators need to seriously considers adaptive regulatory tools due to the dual long memory in returns and downside risk necessitates. To the author´s knowledge, this study is the first to bridge a critical gap in the literature by systematically integrating long memory effects with downside risk metrics so as to provide a more robust framework for understanding risk-return dynamics, as it accounts for the persistent nature of volatility and tail risk, which traditional short-memory models often overlook.

The national development plan (NDP) clearly states that communities should take initiatives to solve their own problems. One of the socio-economic problems faced by communities in South Africa is the effects of inequality, which is largely the apartheid era is responsible for. In trying to deal with the socio-economic problem of inequality, many communities in South Africa have resorted to forming and running cooperatives in different sectors of the economy. However, these cooperatives are reported to be facing numerous challenges, and in some cases, many of them fail or close down. The challenges mentioned range from a lack of capacity to manage, poor economic performance in the country, heavy reliance on government grants and many others. Despite the challenges faced by cooperatives in South Africa, they are still believed to be a very important tool in dealing with the problem of inequality, which mostly affects rural areas where most of these cooperatives operate. To understand the potential impact of cooperatives in dealing with inequality, this paper uses a qualitative approach. Secondary sources of published information by government agencies and cooperatives themselves will be used, as well as literature published by experts in the area of study. Preliminary findings of the paper are that cooperatives in South Africa are very small in size, and the majority of them are in primary sectors of the economy. If cooperatives are to play a significant role in dealing with inequality, there is a need for them to be capacitated to grow and also participate in upper value chains of the economy.  

Financial markets are a system of complex price dynamics that are often influenced by various nonlinear factors. Traditional linear models such as Autoregressive Moving Average also known as ARMA and the GARCH model often fail to capture the inherent nonlinearities observed in financial market returns and trading volumes which poses significant problems in modelling financial time series. The aim of this study was to investigates the role of threshold nonlinearity in modelling financial market returns and trading volume. Daily share prices and trading volumes from the S&P 500 and the Nasdaq indices spanning from January 2000 to December 2020 were used as the sample financial markets. The findings revealed that the threshold model improves predictive performance over traditional linear models, highlighting the importance of incorporating nonlinearity for understanding the dynamics of financial markets. A threshold autoregressive model was proposed to account for sudden shifts in market conditions and nonlinear dependencies between returns and trading volumes.

Prior research on volatility spillover indicates that financial market spillovers are not uniform across all markets. Their intensity and direction depend on factors such as market size, economic linkages, and the nature of the shock. The aim of this study was to comprehensive investigation of volatility spillovers across five major international equity markets from January 2008 to January 2024, a period encompassing multiple financial crises. Utilizing a conditional volatility model, followed by the construction of a spillover index and Granger causality tests to investigate the direction and magnitude of volatility transmission, the findings of this study revealed that the S&P 500 is a net receiver of volatility, acting as a global shock absorber rather than a primary transmitter. In contrast, European markets and the Nikkei 225 emerged as net transmitters, with a tightly coupled, bidirectional relationship observed between the United Kingdom and European markets. The Shanghai Composite was also found to be nearly neutral, likely due to capital controls. These results reiterated the notion that volatility transmission networks are dynamic and not solely dictated by market size. The findings of this study suggest that asset managers and other investors can significantly increase the value of their portfolios if they adopt a multi-polar risk management framework, moving beyond a United States centric view for international diversification to include more European and Asian markets assets in their portfolios.

Traditional financial models assume constant variance in asset returns however, empirical evidence highlights volatility persistence, which are effectively captured by ARCH and GARCH models. The aim of this study was to examine the heteroskedastic behaviour of stock prices across international financial markets. Using a GARCH (1,1) model with a student’s t-distribution, this study analyses daily stock returns from the NYSE, FTSE 100, Nikkei 225, and Shanghai Composite (SSE) between 2018 and 2023. The findings confirm significant autoregressive conditional heteroskedasticity in all markets, supporting the notion that financial volatility is dynamic and clustered. Developed markets, such as the NYSE and FTSE 100, exhibit lower baseline volatility but more persistent shocks, indicative of market efficiency and stability. In contrast, emerging markets, particularly SSE, demonstrate higher volatility and greater sensitivity to past shocks, reflecting heightened uncertainty. The results re-iterate the importance of incorporating dynamic volatility models in risk assessments. Given the increasing complexity of global financial systems, this study advocates for enhanced regulatory frameworks and adaptive econometric methodologies to account for evolving market structures and external macroeconomic shocks.

High-frequency data offers unparalleled insights into market dynamics, facilitating the analysis of statistical properties, dynamic correlations, and scaling behaviours with a precision previously unattainable. These Statistical Properties are essential framework for understanding and modelling the interactions between global financial markets over time. The aim of this study was to investigate the dynamic conditional correlations and scaling behaviour of high-frequency data from January,1 2010 to December 31, 2020. Using the S&P 500, FTSE 100, Nikkei 225, HKEX and DAX as sampled financial markets, the results revealed significant differences in correlation patterns across the markets, as well as fractal-like scaling behaviour. The relationship between the S&P 500-FTSE 100 and S&P 500-DAX supports trend-following strategies, while the Nikkei 225-HKEX and Nikkei 225-FTSE 100 may be closer to random-walk behaviour. This study advances the frontier of knowledge on high-frequency data and offers insights into the temporal relationships and scaling properties between financial markets.

As globalization continues to reshape the business landscape, the use of Business process outsourcing services and activities is gaining significant popularity among businesses. The exact services and activities that benefits and create a sustainable competitive advantage to organizations remain underexplored and few studies have been conducted in the Southern Africa developing countries including South African mobile operators. The main purpose of this study was to redress the existing knowledge gap by ascertaining the actual services and activities that the mobile telecom operators are outsourcing for sustainable competitive advantage. A structured closed-ended questionnaire was used to collect raw data from 210 employees. Overall, the results indicate that some of the key services and activities outsourced for sustainable competitive advantage included building and managing network infrastructure, network roll-out and management, manufacturing of mobile network equipment, monitoring the mobile network on capacity overload and breakdowns and assembly operations, maintenance of the base stations of mobile network, manufacturing of hard software components and resolving software problems and fleet management. The practical implications of the findings, assist organizational management in making informed and structured decisions through the identification of critical activities and services that contribute to the attainment of a sustainable competitive advantage and performance improvement of the mobile telecom operators.

Financial markets are a system of complex price dynamics that are often influenced by various nonlinear factors. Traditional linear models often do not capture the inherent nonlinearities that exist between them. The aim of this study was to examine the interconnectedness of global financial markets using a multivariate Granger causality framework, focusing the United States, Europe, Asia, and emerging markets. Daily closing share prices spanning 13 years were utilised (January 2010 to December 2023) to analyze the shock transmission dynamics. The findings revealed unidirectional causality from the U.S. to European, Japanese, and Emerging markets, underscoring its dominant role in global financial networks. Conversely, European and Asian markets exhibit no reverse causality on the United States, highlighting asymmetrical interdependence. Notably, China’s Shanghai Composite Granger-causes Emerging market returns, reflecting its rising influence. These insights challenge conventional models that underestimate multilateral linkages, demonstrating that financial integration has intensified bidirectional interactions between Emerging markets and developed markets. By implication, investors should reassess exposure to dominant markets like the U.S. and China’s growing sway over Emerging economies while policymakers should prioritize cross-border spillover monitoring and systemic risk frameworks to address asymmetrical dependencies. Also, institutions must adopt nonlinear models to better capture shock transmission and evolving interdependencies, enhancing resilience against contagion.

The increasing inter relationship between financial markets have led to the discovery of new risk dynamics particularly when considering downside risk. Recent advances in econometric modelling have prompted the search for new insights into the intertemporal dynamics between downside risks and expected stock returns. Therefore, the aim of this study was to investigate this relationship in the S&P 500, the FTSE 100, the DAX, the Nikkei 225, and the TSX Composite, covering the period from January 2000 to December 2023. Using a time-varying transition probability model, the findings revealed consistent negative correlations between downside risk measures and expected returns across all markets, with CVaR exhibiting stronger inverse relationships compared to VaR. These findings challenge the classical risk-return trade-off but align with behavioural explanations where heightened risk aversion during downturns suppresses returns. The regime-switching analysis further uncovers asymmetry in transition probabilities where high-risk regimes persist with a 74% probability, while low-risk regimes show greater stability. By implication, this study provides empirical support for regulatory frameworks prioritizing tail risk mitigation, particularly in volatile markets.

The aim of this study was to investigate phase distribution and phase correlation dynamics across international financial markets to uncover cyclical patterns, synchronization, and contagion effects. The sample financial markets were the S&P 500, DAX, Nikkei 225, FTSE 100, Shanghai Composite, BSE Sensex with daily closing prices ranging from 2018–2023. Using the Hilbert-Huang Transform complemented by Phase Concentration Index, Kuiper tests, and Granger causality, the results reveal distinct phase clustering in the Shanghai and BSE Sensex. Developed markets exhibit lower volatility clustering (S&P 500: 28% high-volatility phases) but stronger cross-market synchronization (S&P-DAX: 0.82 correlation), underscoring their interconnectedness. The S&P 500 emerges as a global anchor, Granger-causing European and Asian markets with 1.8–3.5-day lags. Crisis periods, particularly COVID-19, amplify synchronization (average correlation=0.92), while non-crisis phases show market-specific volatility (correlation=0.65). Emerging markets demonstrate regional co-movement (Shanghai-BSE: 0.73), offering diversification opportunities. The findings highlight the hierarchical structure of global markets, where developed economies drive cycles, while emerging markets remain prone to instability. This study advances phase-based methodologies as tools for systemic risk monitoring, providing insights into crisis contagion and investor strategies balancing short-term synchronization with long-term diversification.

Capital structure defined by the proportion of debt and equity financing has always been an important component in managerial finance due to its relative importance in measuring a company's risk. Arguably, it is among the most controversial topics in corporate finance and has gain recent recognition of its importance when considering the emergence of different sources of financing across industry. The purpose of this study was to explore the relationship between capital structure and a firm's profitability in other to determine whether a significant connection exists between how a firm finances its operations and its financial performance. A comprehensive Lilliefors’s, Kolmogorov Smirnov and Chi square tests analysis were utilised for a sample of 23 peer reviewed journal articles across various industries. The findings revealed that capital structure cannot be used to explain variations in profitability and profitability is independent of capital structure. This paper contributes to the ongoing debate on optimal capital structure by strengthening methodological rigor and some of the existing theories on how capital structure can be used to maximise profitability. It may be very helpful for further studies to first establish the direction of impact and the size effect of the independent variables.