An Examination of Adverse Emotions on Social Media and Their Impact on Dividend Decisions

Abstract

Abstract: Research Question:Research Question: Does adverse emotions on social media influence dividend decisions? Motivation: This study is motivated by the need to understand how corporations navigate the challenges posed by negative emotions—such as disgust, anger, fear, sadness, and hate—expressed on social media. By examining the influence of these emotions on corporate decisions, particularly dividend policy, this research aims to shed light on an emerging field that explores the economic impact of social media-driven emotional responses. Idea: This study examines the relationship between adverse emotions expressed on social media platforms and their consequential influence on firms’ decisions regarding dividend payments. Data: The data for this study is derived from a sample of 2,909 firm-year observations of Fortune 500 firms from 2010 to 2017. Method/Tools: Logit regression is deployed to estimate the influence of adverse emotions on social media on firms’ dividend decisions. Findings: The results show that adverse emotions – disgust, anger, fear, sadness, and hate, on social media negatively affect dividend payment decisions. This analysis additionally illustrates that the impact of adverse emotions on social media on firms’ dividend payment decisions is more prominent in firms exhibiting high growth and less evident in those with higher corporate governance scores. Contributions: Through an examination of the influence of adverse emotions on social media on dividend outcomes, the author extends the research on dividend decisions and offers insights by elucidating how expressions of disgust, anger, fear, sadness, and hate on social media can shape financial strategies. This is particularly pertinent to dividend policies. Beyond its theoretical contribution, the study highlights important practical implications. Firms are advised to systematically monitor and analyse social media sentiment, leveraging tools such as natural language processing and social media analytics, in order to proactively address adverse perceptions that may influence financial policy. Such an approach can safeguard firms’ reputations while enabling more resilient and agile dividend strategies. The study acknowledges limitations related to data scope and sample generalizability, and recommends future research across industries, firm types, and longer time horizons to deepen understanding of how adverse social media emotions affect dividend policy.