Women in leadership and the financial performance of the Fortune 500

Despite more than 30 years of women moving into professional, managerial, and executive positions, very few women have reached the senior ranks of leadership. Although 51% of middle management is female, only 15% of corporate officers in the Fortune 500 are women, and women continue to be sparsely represented in senior leadership positions. Research on this phenomenon has been inconclusive. To further our understanding of women\u27s underrepresentation in senior leadership, the purpose of this study was to determine whether there is a relationship between the financial performance of companies in the Fortune 500 and the number of women in senior leadership positions in these companies. This study examined companies that were in the Fortune 500 each year between 1999 and 2008. Two financial data points for each company, return on equity and total shareholder return, were collected and averaged over the 10-year period. Average return on equity and total shareholder return was expressed in z scores. The z scores were averaged, creating an overall index, sorted from high to low, which reflected financial performance. The list was then divided into quartiles. The number and gender of the corporate officers were obtained for the top and bottom quartiles for each year. Spearman rank-ordered correlations were run to determine whether a relationship existed between the financial performance of Fortune 500 companies and the number of women corporate officers in these companies. The results indicated that there was no correlation between financial performance and the number of women corporate officers. Companies that performed well financially were just as likely to have women corporate officers as were companies that performed poorly. This study contributes to the literature on the gap between parity for women at middle management and the professional level as well as the lack of women in senior leadership. Importantly, this study demonstrated that the gender of corporate officers has no bearing on a company\u27s financial performance

On the optimal marketing aggressiveness level of C2C sellers in social media: evidence from China

Social media has become a widely used marketing tool for reaching potential customers. Because of its low cost, social media marketing is especially appealing to customer-to-customer (C2C) sellers. Customers can also benefit from social media marketing by learning about products and by interacting with sellers in real time. However, a seller's marketing microblogs may backfire on her for dominating the social space. Defining the marketing popularity as the average number of likes each seller receives per marketing-related microblog and defining the marketing aggressiveness level as the proportion of her marketing-related microblogs, this paper empirically quantifies the optimal level of marketing aggressiveness in social media to achieve the maximum popularity. We gather the data from China's largest microblogging platform, Sina Weibo, and the sellers in our sample are from China's largest C2C online shopping platform, Taobao. We find that the empirical relationship between the marketing aggressiveness level and the marketing popularity follows an inverted U-shape curve, where the optimal level is around 30%. In addition, we find a saturation effect of the number of followers on marketing popularity after it reaches around 100,000. Our findings imply that social media marketing should not overlook customers' social needs. Our measure of marketing aggressiveness provides a dynamic business metric for practitioners to monitor so as to improve their marketing and managerial decision making process

Bias and Fairness in Large Language Models: A Survey

Rapid advancements of large language models (LLMs) have enabled the processing, understanding, and generation of human-like text, with increasing integration into systems that touch our social sphere. Despite this success, these models can learn, perpetuate, and amplify harmful social biases. In this paper, we present a comprehensive survey of bias evaluation and mitigation techniques for LLMs. We first consolidate, formalize, and expand notions of social bias and fairness in natural language processing, defining distinct facets of harm and introducing several desiderata to operationalize fairness for LLMs. We then unify the literature by proposing three intuitive taxonomies, two for bias evaluation, namely metrics and datasets, and one for mitigation. Our first taxonomy of metrics for bias evaluation disambiguates the relationship between metrics and evaluation datasets, and organizes metrics by the different levels at which they operate in a model: embeddings, probabilities, and generated text. Our second taxonomy of datasets for bias evaluation categorizes datasets by their structure as counterfactual inputs or prompts, and identifies the targeted harms and social groups; we also release a consolidation of publicly-available datasets for improved access. Our third taxonomy of techniques for bias mitigation classifies methods by their intervention during pre-processing, in-training, intra-processing, and post-processing, with granular subcategories that elucidate research trends. Finally, we identify open problems and challenges for future work. Synthesizing a wide range of recent research, we aim to provide a clear guide of the existing literature that empowers researchers and practitioners to better understand and prevent the propagation of bias in LLMs

Social Media Analysis for Social Good

Data on social media is abundant and offers valuable information that can be utilised for a range of purposes. Users share their experiences and opinions on various topics, ranging from their personal life to the community and the world, in real-time. In comparison to conventional data sources, social media is cost-effective to obtain, is up-to-date and reaches a larger audience. By analysing this rich data source, it can contribute to solving societal issues and promote social impact in an equitable manner. In this thesis, I present my research in exploring innovative applications using \ac{NLP} and machine learning to identify patterns and extract actionable insights from social media data to ultimately make a positive impact on society. First, I evaluate the impact of an intervention program aimed at promoting inclusive and equitable learning opportunities for underrepresented communities using social media data. Second, I develop EmoBERT, an emotion-based variant of the BERT model, for detecting fine-grained emotions to gauge the well-being of a population during significant disease outbreaks. Third, to improve public health surveillance on social media, I demonstrate how emotions expressed in social media posts can be incorporated into health mention classification using an intermediate task fine-tuning and multi-feature fusion approach. I also propose a multi-task learning framework to model the literal meanings of disease and symptom words to enhance the classification of health mentions. Fourth, I create a new health mention dataset to address the imbalance in health data availability between developing and developed countries, providing a benchmark alternative to the traditional standards used in digital health research. Finally, I leverage the power of pretrained language models to analyse religious activities, recognised as social determinants of health, during disease outbreaks

It\u27s Not Where You Start, It\u27s How You Finish: Predicting Law School and Bar Success

In this study, we examine the extent to which academic and student engagement factors explain law school grades and first-time bar exam performance. Applying fixed effects linear and logit modeling, our analysis leverages law student transcript data and responses to the Law School Survey of Student Engagement (LSSSE) among students from a diverse group of 20 law schools to estimate academic performance and odds of bar passage. Most notably, we find that GPA improvement during law school is associated with greater odds of passing the bar exam, particularly among students who struggle the most during the first semester. Furthermore, while we find that LSAT scores and undergraduate GPA are predictive (p \u3c 0.05) of both law school performance and bar success (as in previous research), these effects are quite modest. Based on these findings, we propose and discuss several recommendations. These should be helpful to higher education scholars and practitioners, particularly law school deans, administrators, faculty, and academic support staff