2026-2027 Webcast Lecture Series

Abstract

With the rapid advancement of artificial intelligence technologies, algorithmic facial expression analysis (AFEA) has emerged as a promising methodology to measure emotions. Despite rapid adoption across management subfields, the full scope of AFEA’s theoretical potential remains underexplored. This paper provides a framework that links the AFEA measurement innovation to major opportunities for theoretical advancement around emotions in organizations. We start by describing the methodological basis of AFEA and reviewing its applications in management research to date. We then outline two core capabilities of AFEA—namely, capturing the temporal structure of emotions and detecting inauthentic expressions of emotions—and illustrate how these capabilities can enable theory advancement in management research. After presenting an empirical demonstration of AFEA’s capabilities and a practical step-by-step guide for using it, we conclude by discussing key considerations for realizing the future potential of AFEA research..