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The purpose of this study is to examine the role of chief financial officers’ (CFOs’) gender in financial risk taking of 58 US companies along with the impact of having women board members.

Using a panel data of 58 selected S&P 500 companies during the period 2012-2016, this paper determines whether the gender of CFOs and having women board members play a role in risk-taking behavior of firms.

Firms led by female CFOs are smaller in size with lower net income and net revenue. The panel data analysis shows that the impact of female CFOs on firms’ financial risk is mixed, depending on risk measures used, whereas increasing female board members reduces that risk.

The data used is limited to 58 S&P 500 companies, and two of the three risk-taking measures used in the study, specifically investment in property, plant and equipment (PPE) and debt/equity ratio, may not be applicable to some industries.

The findings provide mixed evidence of risk aversion by females in executive and leadership positions, depending on the measures used and the management responsibilities they undertake (CFO versus board member) with support for the glass cliff phenomenon in which females may be leading financially precarious organizations.

Female CFOs are found to be leading relatively smaller and financially poor-performing firms compared with the male CFO-led firms, thereby giving support to the glass cliff arguments.

The paper examines the role of CFOs’ gender and board diversity in risk taking as measured by the investment in PPE, debt/equity ratio and stock return volatility.

This paper aims to determine possible differences in causes or characteristics between men and women in attaining the CEO position in large publicly listed companies in the USA.

T-test statistic, correlation analyses and logit model were used to determine the role individual factors (tenure in management roles, age of CEOs, number of children, years of education) and the firm-level factor (number of employees, net income) play in determining the likelihood of having a female CEO.

The research results show that years of education, the number of children and the number of employees in the business play significant roles in determining the likelihood of having a female CEO. An increase in the number of children and years spent in education lower the probability of the CEO being a woman, while having greater number of employees raises the likelihood of having a woman CEO.

The findings are applicable to only the largest publicly traded firms in the USA and are not applicable to mid to small publicly listed, private or non-for-profit companies or institutions. This research is a starting point for future research of women and men CEOs of small and mid-size publicly traded and non-publicly traded firms in the USA.

Prior research has shown that having children is detrimental for women in management positions; this research specifically identifies this problem for the CEO position. It also reveals that having more of education does not translate to getting to the CEO position for women.

The impact of childcare cost and childcare responsibilities has generally negatively impacted women in workforce. There has been lack of research on the impact of childcare on women managers in larger US public firms. The purpose of this paper is to determine how childcare costs impact the number of women managers in S&P 500 firms.

The paper employs Driscoll–Kraay panel regression model using childcare data for ten years and the percent of women managers at S&P 500 firms.

The results show that increase in childcare cost leads to decrease in percent of women in management positions when the child is an infant. Interestingly, but plausibly the results also show that for preschool-age children as the cost of childcare increases, there is an increase in percent of women in management. Furthermore, childcare costs are still an impediment to careers of women managers, specifically when the child is an infant. The effect is much less when the child grows from an infant to preschool age.

One limitation of this research paper is that the childcare cost data is not directly from the S&P 500 firms. The percent of women management data used is limited to the largest S&P 500 firms. Also, there is no agreement as to definition of a manager at these firms. Moreover, not only childcare cost, but the quality and availability of childcare are factors that also play a role in decision to work and/or use of childcare.

This paper adds to the existing literature by providing evidence that childcare cost impedes women managers' career growth. This finding is more worrisome given that Covid-19 has had a very disproportionate impact on women with child(dren) in the workforce.

Recommender system approaches such as collaborative and content-based filtering rely on user ratings and product descriptions to recommend products. More recently, recommender system research has focussed on exploiting knowledge from user-generated content such as product reviews to enhance recommendation performance. The purpose of this paper is to show that the performance of a recommender system can be enhanced by integrating explicit knowledge extracted from product reviews with implicit knowledge extracted from analysis of consumer’s purchase behaviour.

The authors introduce a sentiment and preference-guided strategy for product recommendation by integrating not only explicit, user-generated and sentiment-rich content but also implicit knowledge gleaned from users’ product purchase preferences. Integration of both of these knowledge sources helps to model sentiment over a set of product aspects. The authors show how established dimensionality reduction and feature weighting approaches from text classification can be adopted to weight and select an optimal subset of aspects for recommendation tasks. The authors compare the proposed approach against several baseline methods as well as the state-of-the-art better method, which recommends products that are superior to a query product.

Evaluation results from seven different product categories show that aspect weighting and selection significantly improves state-of-the-art recommendation approaches.

The proposed approach recommends products by analysing user sentiment on product aspects. Therefore, the proposed approach can be used to develop recommender systems that can explain to users why a product is recommended. This is achieved by presenting an analysis of sentiment distribution over individual aspects that describe a given product.

This paper describes a novel approach to integrate consumer purchase behaviour analysis and aspect-level sentiment analysis to enhance recommendation. In particular, the authors introduce the idea of aspect weighting and selection to help users identify better products. Furthermore, the authors demonstrate the practical benefits of this approach on a variety of product categories and compare the approach with the current state-of-the-art approaches.