MACHINE LEARNING TEXT ANALYSIS OF CORPORATE DIVERSITY STATEMENTS PREDICTS EMPLOYEES’ ONLINE RATINGS Proceedings Paper

Wang, W, Dinh, JV, Jones, KS et al. (2022). MACHINE LEARNING TEXT ANALYSIS OF CORPORATE DIVERSITY STATEMENTS PREDICTS EMPLOYEES’ ONLINE RATINGS . BEST PAPERS PROCEEDINGS - FIFTY-FIFTH ANNUAL MEETING OF THE ACADEMY OF MANAGEMENT, 2022(1), 10.5465/AMBPP.2022.64

cited authors

  • Wang, W; Dinh, JV; Jones, KS; Upadhyay, SK; Yang, J

authors

abstract

  • Many organizations released public statements to condemn racism and affirm their stance on diversity, equity, and inclusion (DEI), yet little is known about the thematic contents and implications on organizational outcomes. Taking both inductive and deductive approaches, the current research advances our understanding in this area. We first employed novel unsupervised machine learning techniques and comprehensively analyzed the texts of diversity statements publicly released by Fortune 1000 companies. We further found evidence that companies that released (vs. did not release) diversity statements and companies whose diversity statements emphasized identity-conscious (vs. identity-blind) topics were more positively evaluated by their employees online.

publication date

  • January 1, 2022

published in

Digital Object Identifier (DOI)

volume

  • 2022

issue

  • 1