The impact of electronic word-of-mouth on corporate performance during COVID-19

Abstract

AbstractThis study attempts to understand the impact of electronic Word of Mouth (eWOM) on corporate financial performance during the COVID-19 pandemic. A supervised machine learning is used to determine the investors’ sentiment of a news story (eWOM) towards a given company from a long position (buying) investors perspective. Ordinary Least Square (OLS) and dynamic quantile regression are used to test the role of eWOM on financial performance. Results reveal no significant relationship between eWOM and the firm’s financial performance. Similarly, we do not find any evidence of an association between eWOM and corporate performance at different quantiles of financial performance. The findings contribute to the existing literature on eWOM and its impact on the financial performance during specific circumstances or financial crises. This study offers insights to researchers, policymakers, regulators, financial report users, investors, employees, clients, and society.