Tone at the bottom: Measuring corporate misconduct risk from the text of employee reviews

Dennis Campbell, Ruidi Shang

Research output: Contribution to journalArticleScientificpeer-review

Abstract

This paper examines whether information extracted via text-based statistical methods applied to employee reviews left on the website Glassdoor.com can be used to develop indicators of corporate misconduct risk. We argue that inside information on the incidence of misconduct, as well as the control environments and broader organizational cultures that contribute to its occurrence, are likely to be widespread among employees and to be reflected in the text of these reviews. Our results show that information extracted from such text can be used to develop measures that clearly separate high and low misconduct risk firms. In out-of-sample tests, our measures are incrementally useful in predicting corporate misconduct beyond other readily observable characteristics such as firm size, performance, industry risk, violation history, and press coverage. We provide further evidence on the efficacy of our text-based measures of misconduct risk by showing that they are associated with future employee whistleblower complaints even after controlling for these same observable characteristics.
Original languageEnglish
JournalManagement Science
Publication statusAccepted/In press - May 2021

Fingerprint

Dive into the research topics of 'Tone at the bottom: Measuring corporate misconduct risk from the text of employee reviews'. Together they form a unique fingerprint.

Cite this