This article examines the relationship between gender diversity, selected female attributes, and financial performance of FTSE 100 firms in the UK. Drawing on critical mass theory by measuring gender diversity as levels of female representation in the boardroom, this study finds a positive and significant relationship between gender diversity and firm performance. However, the results become highly significant and unequivocal when three or more females are appointed to the board compared to the appointment of two or less females. Further analysis reveals that post‐appointment financial performance is positively related to female age, level of education and where female board members also hold executive director positions. The results remain unchanged after accounting for endogeneity concerns and employing alternative measures of firm performance, namely, return on assets and Tobin's Q.

In recent years, the impact of chief executive officers (CEOs) power on corporate strategies has attracted significant public debate in the academic milieu. In this study, we comprehensively review the academic literature on CEO power in relation to different corporate policies. We conduct a comprehensive review by dividing the literature into four streams: CEO power and firm performance, CEO power and executive compensation, CEO power and firm risk-taking, and finally, CEO power on other corporate strategies. Our review shows that the findings are mixed in relation to the effects of CEO power on firm strategies. Overall, the negative impact of CEO power on firm performance is attributed to agency theory, where CEOs pursue their own vested interests, thereby leading to weak corporate governance. The review reveals that the positive impact of CEO power on corporate outcomes is due to effective board monitoring, a powerful board, and high market competition. Our study also shows that most of the studies have adopted Finkelstein’s (1992) four sources of CEO power but have taken different proxies to measure these powers. We have also identified several gaps in the current studies and recommend avenues for further research.

This paper uses a generalised stacking method to introduce a novel hybrid model that combines a one-dimensional convolutional neural network 1DCNN with extreme gradient boosting XGBoost. We compared the predictive accuracies of the proposed hybrid architecture with three conventional algorithms-1DCNN, XGBoost and logistic regression (LR) using a dataset of over twenty thousand peer-to-peer (P2P) consumer credit observations. By leveraging the SHAP algorithm, the research provides a detailed analysis of feature importance, contributing to the model’s predictions and offering insights into the overall and individual significance of different features. The findings demonstrate that the hybrid model outperforms the LR, XGBoost and 1DCNN models in terms of classification accuracy. Furthermore, the research addresses concern regarding fairness and bias by showing that removing potentially discriminatory features, such as age and gender, does not significantly impact the hybrid model’s classification capabilities. This suggests that fair and unbiased credit scoring models can achieve high effectiveness levels without compromising accuracy. This paper makes significant contributions to academic research and practical applications in credit risk management by presenting a hybrid model that offers superior classification accuracy and promotes interpretability using the model agnostic SHAP framework.

The study looks at the impact that the three dimensions of financial inclusion (FI) (i.e., access, usage, quality) may have on poverty alleviation. In doing so, the study relies on demand and supply-side data to measure Nigeria’s FI. The demand-side data were derived from the 2021 Global Findex data, and the supply side data were sourced from the IMF Access survey database (2004–2021). The supply-side data were analysed using the ordinary least squares regression (OLS), while the demand-side data were analysed using the probit regression model. The study outcomes revealed a negative and significant relationship between financial access and poverty rate, further indicating that those who use financial services are less likely to experience poverty. The study recommends that financial service providers tailor their financial products to align with the educational level of the target population to encourage savings.