The effect of investor sentiment on returns of JSE size-based indices under changing market conditions: Evidence from a Markov regime-switching model

Purpose: The continuous unresolved debate that arises between traditional finance and behavioural finance frameworks has dominated empirical literature in recent years. Despite this, the limited literature extends the debate to size-based indices, especially in emerging markets like South Africa that are characterised by alternating market conditions and sentiment-induced markets. Consequently, the objective of this study is to examine the effect of market-wide investor sentiment on the Johannesburg Stock Exchange (JSE) size-based indices’ returns at bullish/bearish market conditions. Methodology: The Markov regime-switching model for the period April 2007 to March 2025 reveals that market-wide investor sentiment has a regime-specific and time-varying effect on JSE size-based indices’ returns. In bullish/bearish market conditions, investor sentiment has a positive significant effect on JSE size-based indices’ returns. However, the magnitude of such effects seems too great in bearish market conditions. Similarly, the JSE size-based indices’ returns are dominated by the bearish market condition, revealing its non-resilient nature to sentiment-induced markets and market fluctuations. Theoretical contribution: The study contributes to resolving the debate in literature that arises from the efficient market hypothesis and behavioural finance frameworks, by demonstrating that JSE size-based indices present adaptive behaviour as supported by the adaptive market hypothesis. Practical implications: Investors must factor in changing market conditions and sentiment levels in the market when determining whether to invest in JSE sized-based indices as it will contribute positively or negative to prospect returns. Policymakers must devise new policy reforms that curb unstable market conditions and noise trading as it contributes directly to alternating market efficiency. Sustainable Development Goals (SDGs): SDG 8: Decent Work and Economic Growth; SDG 10: Reduced Inequalities; SDG 16: Peace, Justice and Strong Institution

Thin-film CdTe/CdS/ZnO solar cells and the path to affordable clean energy: Simulation-based evidence for sustainable photovoltaic design

Global decarbonization commitments have intensified demand for low-cost, scalable solar technologies, yet the gap between laboratory-scale device physics and real-world deployment economics remains poorly addressed in simulation-oriented research. This study examines the photovoltaic performance of a CdTe/CdS/ZnO thin-film solar cell using one-dimensional numerical simulation in SCAPS-1D, focusing on two parameters with direct implications for manufacturing costs and field performance: absorber layer thickness (0.05–2 µm) and operating temperature (300–400 K). Under standard test conditions (AM1.5G, 1000 W/m², 300 K), the baseline device achieves an open-circuit voltage of 0.9482 V, a short-circuit current density of 10.43 mA/cm², a fill factor of 76.79%, and a power conversion efficiency of 7.60%. Increasing absorber thickness progressively raises both current density and open-circuit voltage through enhanced photon capture and reduced bulk recombination, while the fill factor declines owing to greater series resistance. Rising temperature degrades open-circuit voltage, fill factor, and overall efficiency - from 7.6% at 300 K to 5.7% at 400 K - primarily through an exponential increase in reverse saturation current, whereas short-circuit current density remains largely insensitive to thermal variation. At an absorber thickness of approximately 1.5–2 µm, efficiency approaches 21%, a threshold relevant to the commercial viability of CdTe modules. These findings carry concrete implications for sustainable energy deployment: reducing CdTe absorber thickness without sacrificing efficiency directly lowers material consumption and cadmium usage, easing both environmental and supply-chain concerns. The results provide simulation-based guidance for designing cost-competitive thin-film modules capable of supporting the SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action) objectives, particularly in climate-stressed regions where thermal degradation is a persistent operational challenge. Sustainable Development Goals (SDGs): SDG 7: Affordable and Clean Energy; SDG 13: Climate Actio

Measuring organic social media distribution as a sustainable entrepreneurship practice: A protocol for lead generation research in professional services

Purpose. This paper develops a measurement protocol for evaluating organic social media distribution as a resource-efficient alternative to paid advertising in professional service entrepreneurship. Focusing on business coaching, we operationalize how cross-platform short-form video strategies can be measured through sustainability lenses: economic viability, resource efficiency, and labor conditions. Methodology. We synthesize 25+ empirical studies spanning influencer credibility, platform dynamics, and sustainable entrepreneurship. From this synthesis, we construct a field-ready protocol operationalizing four intervention domains: (1) cross-platform posting without paid amplification, (2) standardized identity cues, (3) psychological friction management, and (4) technical production standards. The protocol specifies variable definitions, data collection procedures, fidelity coding rules, and statistical analysis plans. Theoretical contribution. The protocol integrates influencer marketing theory, platform studies, and entrepreneurship research into a unified framework in which content creation functions simultaneously as a distribution mechanism, a trust-building intervention, and a sustainable business practice. By treating organic distribution as economic sustainability question, we extend sustainable entrepreneurship scholarship into the digital creator economy. Practical implications. For entrepreneurs, the protocol translates credibility constructs into measurable behaviors and testable outcomes. For researchers, it provides standardized procedures enabling rigorous field studies examining whether organic strategies generate economically viable lead flows under resource constraints. For educators, it demonstrates how content creation can be taught as core entrepreneurial competency aligned with sustainable business principles. Sustainable Development Goals (SDGs): SDG 8: Decent Work and Economic Growth; SDG 9: Industry, Innovation and Infrastructur

Health financing and socio-economic predictors of financial vulnerability: Empirical evidence from the United States

Purpose: This study examines how health insurance coverage and medical cost burden, alongside key socioeconomic and demographic factors, predict financial vulnerability in the United States, with implications for sustainable household financial protection aligned with SDG 3.8. Methodology: A repeated cross-sectional design was employed, using three waves of the US National Financial Capability Survey (NFCS: 2018, 2021, 2024), covering the pre-pandemic, post-pandemic, and recovery periods, respectively. A multidimensional Financial Vulnerability Index (FVI) was constructed following a three-dimensional framework (sensitivity, resilience, exposure) and operationalised as a binary variable using a composite standardised scoring approach. Binary logistic regression models with pairwise comparisons among all predictor categories were estimated for each survey wave. Results: Uninsured individuals face 37-50% higher odds of financial vulnerability relative to uninsured counterparts, while individuals with high medical cost burdens face over four times the odds (OR = 4.18-5.34) across the survey waves. Household income emerges as the single most powerful predictor, with individuals earning less than $25,000 being 8.83 to 16.0 times more likely to be financially vulnerable than those earning$200,000 or more. Education, financial literacy, and household dependency exhibit threshold effects: meaningful protective differences emerge only upon attainment of university-level education or reduction to zero dependents. Theoretical contribution: The study extends Grossman's (1972) Health Capital Model into the domain of financial vulnerability by demonstrating that market-based health financing structures interact with socioeconomic position to generate structural exposure to financial hardship. Rather than adopting a unidimensional index, the application of multidimensional FVI in this study advances methodological practice in financial vulnerability research and reveals threshold effects previously masked in the literature. Practical Implications: The findings call for health and fiscal policy frameworks that extend beyond aggregate economic metrics to address distributional consequences of health financing arrangements. Targeted interventions, including expanded insurance coverage, income support for low-income households, financial literacy programmes, and strengthened social protection for working-age adults, are identified as critical for reducing persistent financial vulnerability and advancing financial sustainability. Sustainable Development Goals (SDGs): SDG 3: Good Health and Well-being; SDG 10: Reduced Inequalities; SDG 1: No Povert

An algorithmic framework for sustainable marketing audit and prioritization in consulting engagements: The PRISM-Bridge Model

Purpose: This paper develops a reproducible algorithmic protocol, the PRISM-Bridge Model, that converts comprehensive marketing audit findings into a transparent, sequenced, and measurable action plan in consulting engagements. Crucially, the framework explicitly embeds measurement readiness and strategic sustainability logic into prioritization decisions, aligning short-term corporate actions with Environmental, Social, and Governance (ESG) criteria and the United Nations Sustainable Development Goals (specifically SDGs 8, 9, and 12). Methodology: The study employs a conceptual-development design grounded in a structured synthesis of established research streams, including marketing audit theory, multi-criteria decision analysis (AHP), and sustainable business strategy. To validate the mechanics of the algorithm without overstating empirical claims, the framework is applied to an illustrative demonstration backlog of 30 typical digital marketing and consulting initiatives. Results: The proposed algorithm produces four interconnected deliverables: a structured intervention register, a weighted multi-criteria priority score (combining impact, effort, risk, dependency load, and sustainability alignment), a dependency-aware three-phase roadmap, and a constrained quick-win portfolio. Demonstration results confirm that the model systematically prevents the execution of initiatives that conflict with sustainable organizational development, while maintaining strategic breadth. Practical and Theoretical Implications: Theoretically, the study addresses a persistent integration gap by unifying audit diagnostics, multi-criteria prioritization, and sustainable execution into a single decision-and-delivery architecture. Practically, the PRISM-Bridge Model provides consulting teams with a reusable governance instrument that reduces arbitrariness in early project decisions, improves client explainability, and establishes a practical bridge between diagnostic audits and performance-oriented execution aligned with long-term sustainable development. Sustainable Development Goals (SDGs): SDG 8: Decent Work and Economic Growth; SDG 9: Industry, Innovation and Infrastructure; SDG 12: Responsible Consumption and Productio

Employment status and income level as determinants of personal income tax compliance: Evidence from South Africa

Abstract: Purpose. This study examines the influence of demographic factors on personal income tax (PIT) compliance among taxpayers in Mbombela, Mpumalanga, South Africa, to address persistent revenue shortfalls that undermine government fiscal capacity. Methodology. Employing a positivist research philosophy and cross-sectional survey design, the study utilized a random sample of 103 taxpayers from a population of 2,679 registered taxpayers. Data were collected through a structured questionnaire and analyzed using descriptive statistics and multiple regression analysis in SPSS and STATA. The Slippery Slope Framework, Fiscal Exchange Theory, and Political Legitimacy Theory provided the theoretical foundation. Results. Regression analysis revealed that only employment status (β = 0.168, p < 0.001) and monthly income (β = 0.099, p = 0.001) significantly influence tax compliance. Age, gender, educational background, household size, and SARS registration did not demonstrate significant effects (p > 0.05). Descriptive analysis revealed pervasive non-compliance: 60.2% of respondents reported failing to pay all taxes owed, and 62.1% admitted to incomplete income disclosure. Theoretical contribution. This study challenges conventional assumptions regarding demographic determinants of tax compliance, demonstrating that structural factors - particularly PAYE withholding mechanisms - outweigh individual demographic characteristics. The findings support the Slippery Slope Framework’s emphasis on power-based enforcement while highlighting that institutional legitimacy deficits, rather than demographics, drive non-compliance. Practical implications. Results indicate that SARS and policymakers must extend interventions beyond demographic targeting to address institutional legitimacy, perceived fiscal reciprocity, and equitable enforcement. Recommendations include extending PAYE-style withholding to additional income sources, enhancing transparency, and developing sector-specific compliance strategies. Originality/value. This study provides the first systematic empirical analysis of demographic determinants of PIT compliance in Mbombela, Mpumalanga, demonstrating that institutional factors merit greater attention than demographic segmentation in compliance enhancement strategies. Sustainable Development Goals (SDGs): SDG 16: Peace, Justice and Strong Institutions; SDG 10: Reduced Inequalities; SDG 8: Decent Work and Economic Growt

Assessing rural farmers’ climate vulnerability in Gambia: A PCA‑based index for sustainable development

Purpose. This paper assesses the climate vulnerability of 400 smallholder farming households in rural Gambia by integrating exposure, sensitivity, and adaptive capacity into a composite vulnerability index, providing policy-relevant insights for climate-resilient development. Methodology. Household survey data from three rural regions (North Bank, Central River, Upper River) are analyzed using Principal Component Analysis (PCA) to derive data-driven weights for 23 indicators. The index is validated through associations with NGO support, government assistance, insurance, credit access, and agricultural extension. Results. North Bank exhibits the highest vulnerability (VI = -6.37) driven by low adaptive capacity despite minimal climate exposure. Upper River shows lower vulnerability (VI = +1.56) despite high climate exposure, owing to better socio-economic conditions. Validation reveals that NGO support and insurance reduce vulnerability (r = -0.82, -0.94), whereas government support paradoxically correlates positively (r = 0.79), likely reflecting endogenous targeting. Theoretical contribution. The study advances vulnerability assessment literature by applying PCA-based weighting to household-level data in a low-income African context, demonstrating that adaptive capacity is more decisive than biophysical exposure. Practical implications. Findings emphasize prioritizing investments in education, infrastructure, credit, and insurance over exposure-focused interventions. The index supports policy prioritization under Gambia’s Nationally Determined Contributions and National Adaptation Plans, enabling regional differentiation of adaptation strategies. Sustainable Development Goals (SDGs): SDG 1: No Poverty, SDG 2: Zero Hunger, SDG 13: Climate Actio

Household savings shift in India: Financial inclusion, banking stability, and sustainable capital markets

Purpose. This study investigates India’s household investment transformation (2015–2025), examining the shift from bank deposits to market instruments and its implications for financial inclusion, banking stability, and sustainable capital formation. Methodology. Mixed-methods longitudinal design combines descriptive analysis with econometric hypothesis testing. Household data from the Reserve Bank of India, SEBI, and All-India Debt and Investment Survey (48,000 observations) are analysed using logistic regression and time-series models. Results. Household deposit share declined from 48 per cent to 25 per cent, while market instruments rose from 40 per cent to 63 per cent. High financial literacy, income, and urban residence increase market participation by 18.8, 31.2, and 25.1 percentage points, respectively. However, participation disparities persist: urban residents and high-income households account for 55–58 per cent of investors, versus 35–12 per cent of the population. Declining deposit ratios are associated with slower credit growth to the MSME and renewable energy sectors - sectors critical to sustainable development. ESG-classified IPOs exhibit lower underpricing and superior long-run returns, suggesting that sustainability disclosure influences valuation. Theoretical and Practical Contributions. The study extends sustainable finance theory by linking household financial behaviour to macro-level resilience. Recommendations include targeted financial literacy programmes, diversifying bank funding to maintain credit supply to the SDG sector, and strengthening ESG disclosure standards to align retail investment flows with sustainable development objectives. Sustainable Development Goals (SDGs): SDG 10: Reduced Inequalities; SDG 8: Decent Work and Economic Growth; SDG 13: Climate Actio

Application of material flow cost accounting to achieve environmental sustainability in small-scale soybean oil production in South Africa

Purpose: This study investigates the effectiveness of Material Flow Cost Accounting (MFCA) as a tool for enhancing resource efficiency and environmental sustainability in small-scale soybean oil production in South Africa. The main objective is to analyze the impact of MFCA on waste reduction and economic performance within the production process. Methodology: A case study approach was adopted, focusing on the implementation of ISO 14051 in a small-scale soybean oil production setting. Data were collected through direct observation over three months, capturing cost information, inputs, and outputs across the various production stages. Results: The findings indicate that a significant portion of waste generated during soybean oil production can be reused as by-products in other processes. The application of MFCA led to notable cost savings and promoted environmental sustainability. Specifically, the technique resulted in total savings of 6,083.05 Rands, with 196.97 Rands saved in dehulling, 4,609.08 Rands in drying, 350 Rands in oil extraction, and 927 Rands in filtration. The study also highlights the potential for revenue generation and improved resource utilization through waste minimization and reuse. Theoretical Contribution: This research contributes to the literature by demonstrating the value of MFCA in agri-food production, emphasizing its role in reducing waste, costs, and energy use while supporting sustainability objectives. Practical Implications: The results provide actionable recommendations for industry practitioners, policymakers, and scholars, advocating for the adoption of MFCA in soybean oil and similar production processes to achieve sustainable development goals. Sustainable Development Goals (SDGs): SDG 2: Zero Hunger; SDG 6: Clean Water and Sanitation; SDG 7: Affordable and Clean Energy; SDG 9: Industry, Innovation and Infrastructure; SDG 12: Responsible Consumption and Production; SDG 13: Climate Action; SDG 15: Life on Land; SDG 17: Partnerships for the Goal

Impact of Industry 4.0 technologies on the financial performance of manufacturing companies: an empirical study in Cameroon.

Purpose. This study aims to assess the impact of Industry 4.0 technologies - specifically Big Data, Internet of Things (IoT), collaborative robots, and Cyber-Physical Systems (CPS) - on the financial performance of manufacturing companies in Cameroon, addressing the research gap in the Sub-Saharan context. Methodology. Adopting a quantitative approach, primary data were collected via questionnaires from 104 manufacturing firms. The study employed Chi-square tests and binary logistic regression to analyse the relationship between technological adoption and key performance indicators, including Return on Assets (ROA), Return on Equity (ROE), turnover, and productivity. Results. The empirical findings indicate that integrating Big Data and IoT has a statistically significant positive effect on all measured financial indicators. Collaborative robots positively impact turnover, whereas Cyber-Physical Systems showed no significant correlation with financial performance in the studied context. The theoretical contribution. This research extends economic production theory to developing economies. It provides empirical evidence that digital transformation serves as a critical production input, significantly enhancing firm output and challenging the “IT productivity paradox” in African manufacturing sectors. Practical implications. The study suggests that manufacturing leaders in developing regions should prioritise investments in Big Data and IoT for immediate efficiency gains. Furthermore, it advocates for government-led subsidy policies to lower entry barriers for automation and foster international competitiveness. Sustainable Development Goals (SDGs): SDG 8: Decent Work and Economic Growth; SDG 9: Industry, Innovation and Infrastructur