What Drives Employee Strategic Salience in Shareholder Communications? A Machine Learning Approach

Whereas research across disciplines has conclusively established the critical contribution of employees to firm success, a comprehensive analysis of how companies communicate about their employees remains notably absent. In this study, we introduce a novel text-based machine learning approach that allows us to quantify the strategic salience of stakeholders from organizational communications. We then apply our approach to billions of words from shareholder-oriented documents, such as 10-Ks and earnings calls, to analyze employee strategic salience across firms and industries. We observe that cross-sectional variation in employee salience predicts employee sentiment toward the firm and employment-related regulatory violations. Subsequently, we hypothesize that employee strategic salience is higher in human capital–intensive industries and when labor markets are tighter but lower when executives are short-term-oriented and firm product markets are uncertain. We find support for our hypotheses. Further analyses show that employee strategic salience decreases during periods of industry downturns and intense competition but particularly rose during the COVID-19 pandemic. Our study provides a scalable methodology for assessing organizational text and communications and contributes to scholarship on human capital and shareholder governance.