The academic study of grammatical voice (e.g., active and passive voice) has a long history in the social sciences. It has been examined in relation to psychological distance, attribution, credibility, and deception. Most evaluations of passive voice are experimental or small-scale field studies, however, and perhaps one reason for its lack of adoption is the difficulty associated with obtaining valid, reliable, and replicable results through automated means. We introduce an automated tool to identify passive voice from large-scale text data, PassivePy, a Python package (readymade website: https://passivepy.streamlit.app/). This package achieves 98% agreement with human-coded data for grammatical voice as revealed in two large validation studies. In this paper, we discuss how PassivePy works, and present preliminary empirical evidence of how passive voice connects to various behavioral outcomes across three contexts relevant to consumer psychology: product complaints, online reviews, and charitable giving. Future research can build on this work and further explore the potential relevance of passive voice to consumer psychology and beyond.
