aceModerate evidence
Published recordLifestyleGood Space
More
Avatars that engage: How visual role expectations, contextual congruence, user characteristics, and perceived sympathy shape responses to shopping chatbots
- Truvace record
- TRV-2026-2685raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Randomized controlled trial
- Sources
- 1 sourcePeer-reviewed
- sha256
- fc8c2cf45289…check it
In online shopping contexts, chatbot avatars with human-like features can conform to or conflict with users' contextual role expectations - schemas of what a salesperson in a given retail domain should look like. However, whether users' actual avatar preferences align with their articulated role expectations, and whether avatar–context congruence translates into stronger engagement intentions, remains unclear. In a large-scale between-subjects experiment (N = 5278; data collected 2024 in the United States, Unite…
The gain
In an online experiment with 5,278 people in the US, UK and Switzerland, shopping-chatbot avatars matching a person's own idea of a salesperson earned slightly higher intention to use the chatbot than mismatched ones (3.54 vs 3.39 on a 6-point scale).
What this doesn’t fix
Effects were small and intention was self-reported right after a scripted chatbot interaction, with sympathy and intention measured at the same time, so the link from sympathy to intention is not firmly established. The samples came from three Western countries only.
Sources
1 on file- Computers in Human Behavior: Artificial Humansdoi:10.1016/j.chbah.2026.100335
Cite this record
v1Truvace Impact Record TRV-2026-2685, v1: “Avatars that engage: How visual role expectations, contextual congruence, user characteristics, and perceived sympathy shape responses to shopping chatbots.” Truvace, 2026-11-12. /record/TRV-2026-2685 (accessed on the day you copy it). sha256 fc8c2cf452893ed4…
Change history
1 versionEvery change made to this record since it was first published, and why. Nothing has changed since it went up.
Certified into the record
Check this record hasn’t been altered
How to check it yourself, without trusting this page
Download any version from /api/record/TRV-2026-2685 and hash its canonical field yourself, for example with shasum -a 256. You should get its content_hash. Each version’s text also ends with prev:and the hash of the version before it (version 1 points to 64 zeros). If anyone had changed a single character of any version after it was saved, those hashes wouldn’t line up.