Abstract

The academic study of grammatical voice (e.g., active and passive voice) has a long history in the social and psychological sciences. Passive voice, for example, has been used to identify victim blaming in traumatic events, false versus truthful speech patterns, and levels of construal. 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.

In the first part of this project and using machine-learning methods, we introduce an automated tool to identify passive voice from large-scale text data, PassivePy. With minimal computational overhead, this package achieves 97% agreement with human coded data for grammatical voice as revealed in two large validation studies. In this part, we discuss passive voice as an important psychological construct, how PassivePy works, and how researchers studying psychological distancing, text-as-data, and grammatical voice can use PassivePy in consumer psychology and beyond.

In the second part, we apply PassivePy to examine use of passive voice in consumer complaints. Customers often have negative service experiences. But might a subtle way customers describe such experiences shed light on how likely they are to voice or escalate their complaints (e.g., share negative word of mouth or dispute an offered resolution)? In the second part of this project and using a multimethod exploration, combining automated textual analysis of over 160K consumer complaints with experiments, we demonstrate the important role of passive voice. Consumers who complain using passive voice are more likely to spread negative word of mouth or dispute the resolution offered by the company. Greater use of passive voice indicates consumers attribute more fault to the company (rather than themselves), which leads them to voice or escalate their complaint. These findings shed light on an understudied aspect of language (i.e., linguistic structure), deepen understanding of how language reflects attitudes and intentions, and provide insight into how managers should respond to unhappy customers. 

Please contact dip.mkt@unibocconi.it if you wish to attend.