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Too little, too much, or just right? How AI recommendation information volume shapes trust and consumer behavior
- Truvace record
- TRV-2026-2485raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Randomized controlled trial
- Sources
- 1 sourcePeer-reviewed
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Artificial intelligence (AI) shopping assistants explain why a product is selected, yet the appropriate defaults remain unclear. We define recommendation information volume as the amount of nonredundant, product-relevant information and compare five ordered explanation packages. Study 1 identifies an inverted-U response in a randomized Chinese mobile-retail experiment. Study S1 compares identical messages attributed to an AI assistant or a human salesperson, with separate source-neutral measurement validation. S…
The gain
In a series of studies including a randomized Chinese mobile-retail experiment and data on 51,248 recommendation impressions, moderate-sized AI explanation packages performed best among the tested sets.
What this doesn’t fix
Same-session estimates do not establish causal mediation, and because amount, diagnostic priority and content composition vary jointly, the experiments identify assigned-package effects, not pure quantity effects.
Sources
1 on file- Journal of Retailing and Consumer Servicesdoi:10.1016/j.jretconser.2026.105205
Cite this record
v1Truvace Impact Record TRV-2026-2485, v1: “Too little, too much, or just right? How AI recommendation information volume shapes trust and consumer behavior.” Truvace, 2026-11-12. /record/TRV-2026-2485 (accessed on the day you copy it). sha256 7b01c96b7fb8361c…
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