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When friendly AI backfires: Relationship norm misfit in chatbot service recovery
- Truvace record
- TRV-2026-2639raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Controlled or validated comparison
- Sources
- 1 sourcePeer-reviewed
- sha256
- 28633fe8307c…check it
This study identifies a context-specific boundary condition within established frameworks governing chatbot service recovery: the effectiveness of warm, relational recovery cues depends on alignment with the relationship norms operative in the exchange context. Drawing on Relationship Norms Theory and Justice Theory, we develop and test a norm-contingent inference account in which recovery effectiveness is governed by alignment between chatbot framing and the dominant relationship logic of the exchange. Across f…
The problem
In four controlled experiments on e-commerce failure scenarios, warm, communal chatbot recovery framing reduced customers' forgiveness, repurchase intention and behavioural acceptance when exchange norms were salient.
What this doesn’t fix
The findings are limited to four experiments using exchange-oriented e-commerce failure scenarios.
Sources
1 on file- Computers in Human Behavior Reportsdoi:10.1016/j.chbr.2026.101409
Cite this record
v1Truvace Impact Record TRV-2026-2639, v1: “When friendly AI backfires: Relationship norm misfit in chatbot service recovery.” Truvace, 2026-11-12. /record/TRV-2026-2639 (accessed on the day you copy it). sha256 28633fe8307c0ff0…
Change history
1 versionEvery change made to this record since it was first published, and why. Nothing has changed since it went up.
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