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This study aims to investigate the association between the co-option of the chief financial officer (CFO) and dividend payments, assessing whether the talent of the CFO affects this association.

The empirical analyses were based on hand-collected data for 922 firm-year observations from 157 European listed firms, during the period 2013–2019. Empirical models, based on a two-step estimation procedure, involved the use of instrumental variables and the generalised moment method.

The results show that CFO co-option is negatively associated with the level of dividend payments. It was also found that the degree of CFO talent moderates the negative association between CFO co-option and dividend payments.

This investigation responds to the call for literature which examines how chief executive officer (CEO) – CFO relationships influence firms’ policies and outcomes. The study offers novel evidence for the individual-level characteristics of CFOs which are likely to reduce the effectiveness of CEO power and increase monitoring on corporate decisions on dividends.

The study sheds light on the effect of the interactions between CEOs and CFOs, which are important for investors’ expectations. In this regard, investors may be interested in the CFO profiles which may reduce CEO power over dividend policies.

Unlike previous research, which focused on CEOs, the authors are the first to shed light on the role of CFOs as key decision makers in influencing the dividend policies in modern corporations.

The purpose of this study was to examine the moderating role of institutional ownership on the relationship between board gender diversity and earnings management (EM) among listed firms in East African Community (EAC) partner states.

The study used a sample of 71 firms listed in the EAC partner states over 2011–2020. Data were handpicked from the individual firm's audited annual financial reports. Based on the results of the Hausman test, the study used the results of the fixed-effect regression model to test the hypotheses. To test the robustness of the results, the study employed an alternative measure of EM and two additional econometric techniques, including the pooled ordinary least squares (OLS) and the system generalized method of moments (GMM).

The empirical findings revealed that female directors improve the board's effectiveness in monitoring managerial roles. Specifically, the results showed a significantly negative relationship between the proportion of women in the corporate board and EM (as measured by discretionary accruals (DAs)). The findings further revealed an inverse relationship between the proportion of institutional ownership and EM. Finally, the results further demonstrated that institutional ownership enhances the role of board gender diversity in mitigating EM among listed firms in the EAC.

The findings of this study may be useful to managers, investors and regulators in assessing the role of institutional ownership and women's participation on corporate boards as a strategy for alleviating unethical manipulation of earnings.

The findings of this study contribute to the growing concern on gender inequality, especially the marginalization of women from the paid labor force and decision-making. The findings highlight the importance of having more women in the corporate board since this may help in mitigating corporate fraud. Similarly, the findings highlight the importance of institutional ownership as a corporate governance (CG) tool.

Previous studies have reported mixed empirical results on whether board gender diversity mitigates EM. To the best of the author's knowledge, this is the first paper to fill the existing gap by exploring whether institutional ownership moderates the relationship between board gender diversity and EM among listed firms in the EAC.

This study aims to examine the impact of board characteristics on firm performance while also exploring the influential mechanisms that help Chinese listed companies establish effective boards of directors and strengthen their corporate governance mechanisms.

This paper uses machine learning methods to investigate the predictive ability of the board of directors' characteristics on firm performance based on the data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges in China during 2008–2021. This study further analyzes board characteristics with relatively strong predictive ability and their predictive models on firm performance.

The results show that nonlinear machine learning methods are more effective than traditional linear models in analyzing the impact of board characteristics on Chinese firm performance. Among the series characteristics of the board of directors, the contribution ratio in prediction from directors compensation, director shareholding ratio, the average age of directors and directors' educational level are significant, and these characteristics have a roughly nonlinear correlation to the prediction of firm performance; the improvement of the predictive ability of board characteristics on firm performance in state-owned enterprises in China performs better than that in private enterprises.

The findings of this study provide valuable suggestions for enriching the theory of board governance, strengthening board construction and optimizing the effectiveness of board governance. Furthermore, these impacts can serve as a valuable reference for board construction and selection, aiding in the rational selection of boards to establish an efficient and high-performing board of directors.

The study findings unequivocally demonstrate the superiority of nonlinear machine learning approaches over traditional linear models in examining the relationship between board characteristics and firm performance in China. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. The study reveals that the predictive performance of board attributes is generally more robust for state-owned enterprises in China in comparison to their counterparts in the private sector.