This study examines the impact of Artificial Intelligence (AI)-enhanced Environmental, Social, and Governance (ESG) reporting on firm valuation in emerging markets. It aims to explore how AI integration enhances the interpretability and predictive accuracy of ESG metrics in shaping market perceptions and investor decisions. This study employs a panel dataset from 2018 to 2024, analysing publicly listed non-financial firms across five major sectors: manufacturing, energy, telecommunications, consumer goods, and industrials. This study contributed by employing AI-powered multimodal analysis with conventional ESG scoring methods and integrating Fixed-Effects Regression with machine learning (ML) algorithms including Extreme Gradient Boosting (XGBoost) and Random Forest to identify complex, non-linear relationships within ESG data and firm valuation. The results show empirical evidence that integrating ML enhances the explanatory power of ESG data. Findings indicate that ESG performance is positively correlated with higher market valuations, particularly in Environmental and Social dimensions. Governance metrics are more inconsistent, which may be due to heterogeneity in governance practices, regulatory enforcement and the challenges of quantifying governance quality beyond compliance indicators across the focus emerging markets. Firms identified in ESG controversies tend to face valuation penalties, which stresses market sensitivity to reputational risks. ML algorithms outperform conventional techniques in predictive accuracy, revealing complex, non-linear interactions within ESG data. This study contributes to both the academic literature and practice showing how next-generation ESG reporting can robust valuation models, address limitations of conventional ESG scoring, and ensure a reliable outlook for investors and policymakers of industries in emerging markets.

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Orientation: Managers are supposed to manage companies to maximise shareholders’ wealth. Instead, there are long-standing perceptions that managers are rent extractors who maximise their own wealth, implying a misalignment between executive remuneration and company performance. Research purpose: The purpose of this study was to determine the existence of a relationship between executive remuneration and the financial performance of South African-listed mining companies. Motivation for the study: Executive remuneration significantly increased over the past five decades relative to company financial performance. The mining sector was selected due to its susceptibility to external factors and shocks leading to volatility in the financial performance of mining companies. Research approach/design and method: This study was a quantitative archival study, using data from 2015 to 2021, by applying the hierarchical linear modelling technique at a 95% confidence level and a 5% significance level. Main findings: The study found a weak to strong relationship between executive remuneration and company financial performance. Furthermore, an analysis of executive remuneration revealed an increase in short-term incentive payments and a decrease in the fixed salary as a proportion of chief executive officer remuneration. Practical/managerial implications: Because the study found a strong link between executive remuneration and earnings-based financial performance metrics, governing bodies should ensure that financial performance metrics include cash flow-based financial metrics as company earnings are highly susceptible to management manipulation. Contribution/value-add: This study contributes to the existing literature on executive remuneration and will be useful to researchers, shareholders, boards of directors, remuneration committees and policymakers.

Orientation: The study highlights further use of accounting ratios by considering it as the main factor for value detecting, value enhancing and value sustainability tool. This is carried out by investigating the effect of accounting ratios on the performance of the listed firms in Nigeria.Research purpose: The study examining the relationship between accounting ratios and firm value sustainability. It further studied the effect of accounting ratios on firm’s value sustainability.Motivation for the study: The study provoked an insight that accounting ratios should not only be used to analyse and interpret company’s financial health but also be used as a tool that guides companies’ operation for value sustainability.Research approach/design and method: The study employed data retrieved from sampled listed firm’s annual reports and centered the study on agency and signaling theories. The data were evaluated using descriptive analysis, Pool ordinary least square (OLS), random effect and panel generalised method of moment (GMM). Thirty firms representing all sectors except the financial sector from 2008 to 2017 were sampled using a stratified sampling method. The dependent variable is Tobin’s q, whilst the explanatory variables are also proxy by accounting ratios.Main findings: The study revealed that accounting ratios do not only affect and influence firm value but also help to detect if value had been created or not, as well as if the value created has been sustained over years. It further revealed a significant and positive relationship between ratios used in the study (current ratio, ROA, asset turnover, debt-equity ratio and earnings per share) and firm’s value.Practical/managerial implications: The study therefore recommended that a comprehensive accounting ratio that comprises both conventional accounting ratios and sustainability accounting ratios should be published. This should be published alongside with the firm’s financial statement for ease of interpretation and determination of the yearly performance by any stakeholder who may want to interpret the report for an informed decision.Contribution/value-add: It then stated that a before tax sustainability rate law should be enacted for easy determination of sustainability ratio. The study contributed to decision makers, Accounting Profession, Corporate Organizations and Government.

Environmental, Social and Governance (ESG) risk is increasingly influenced by inter-firm relationships embedded in global supply chains. Challenging firm-level approaches that treat ESG exposure as independent across companies. This study examines whether firms’ structural positions within supply-chain networks are associated with ESG risk exposure and whether incorporating network information improves ESG risk prediction. The analysis draws on an international dataset integrating validated supplier-buyer relationships, shipment-level trade data. ESG incident records and sentiment derived from ESG-related news. Network-based econometric models and graph-oriented learning approaches are evaluated against conventional firm-level benchmarks. The results indicate that ESG risk clusters within connected groups of firms, with higher exposure observed among firms occupying central or intermediary positions in supply networks. In addition, ESG-related media sentiment exhibits predictive power for subsequent ESG incidents, supporting its role as an early warning signal. Overall, models that explicitly account for network structure deliver more accurate and better-calibrated predictions than standard econometric and machine-learning approaches. These findings highlight the value of a network-informed perspective for ESG risk assessment in complex international production systems.

Academic counseling plays a crucial role in student support within higher education institutions, contributing not only to academic achievement but also to career development and entrepreneurial initiatives. This study investigates the impact of academic advising on fostering entrepreneurial intentions among students at the Durban Institute of Technology (DUT) in South Africa. Utilizing planned behavioral theory as a conceptual framework, this research explores how academic guidance affects students' attitudes toward entrepreneurship, subjective norms, and perceived behavioral control, ultimately influencing their intention to engage in entrepreneurial activities. Data analysis conducted using SPSS 30.0 revealed a significant positive correlation between academic counseling and entrepreneurial intentions (r = 0.612, p < 0.001). Key findings indicate that academic advising enhances entrepreneurial awareness and provides essential information while also highlighting a gap in connecting students with practical resources, such as business planning and funding support. Additionally, this study reveals that Generation Z students prefer digital advisory tools, although they still value traditional learning methods for guidance. Recommendations include expanding mentoring programs, enhancing collaboration between universities and industries, and leveraging technology to improve advisory services. Moreover, addressing gender-influenced and culturally integrated advisory practices can support underrepresented student groups in entrepreneurship. Future research should focus on the longitudinal effects of academic advising on entrepreneurial success and explore best practices for integrating entrepreneurship into academic counseling models.