The Unexpected Mirror: How AI Communication Style Shapes Consumer Self-Perceptions
(A2026-133392)

AUTHORS

Itai Linzen, Tel Aviv University; Aner Sela, University of Florida; Yael Steinhart, Tel Aviv University; Ziv Carmon, INSEAD

ABSTRACT

The rise of large language models has opened the door to a new kind of consumer interaction, where AIs can converse in nuanced linguistic styles. This research explores how AI’s communication style shapes the way consumers see themselves. Across three studies (N = 3,404), participants who interacted with an extraverted AI reported more positive self-perceptions than those interacting with an introverted AI. This shift appears to stem from linguistic matching: people tend to mirror the AI’s style, adopting its extraverted tone and, in doing so, internalizing some of its upbeat emotional state. These enhanced self-perceptions, in turn, boost word-of-mouth and return intentions.‎ These trends were stronger when participants conversed with an AI relative to another human (actually an AI). In addition, the effect was moderated when participants' language style was manipulated to differ from the AI's, such that linguistic mirroring was attenuated.