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Three experiments find AI shopping advice works best when its role fits the product

Study title: Emotional Resonance or Rational Guidance? Exploring the Mechanisms Through Which Generative AI Recommendation Roles Shape Consumers' Adoption Intentions

Evidence type
Controlled or validated comparison
Sources
1 sourcePeer-reviewed
Published
2 min read
Truvace record
TRV-2026-2653cite or check it

Study title: Emotional Resonance or Rational Guidance? Exploring the Mechanisms Through Which Generative AI Recommendation Roles Shape Consumers' Adoption Intentions

Researchers ran three experiments on how a generative AI recommender's social role affects whether consumers say they would adopt its advice. In Study 1 (n = 263), assistant-type recommendations produced higher adoption intentions for search goods, while friend-type recommendations did so for experience goods.

Read the study abstract

ABSTRACT Generative artificial intelligence (GenAI) is reshaping e‐commerce by enabling conversational recommenders that can explain their advice and flexibly enact social roles (e.g., assistant vs. friend). Yet it remains unclear how these roles affect consumers' adoption of GenAI recommendations, through which trust pathways, and under what message designs. Drawing primarily on role theory and trust theory, with product evaluability and dual‐process logic specifying the relevant conditions, we examine how reco…

Emotional Resonance or Rational Guidance? Exploring the Mechanisms Through Which Generative AI Recommendation Roles Shape Consumers' Adoption Intentions
"Container Port Cranes, Southampton Docks - 2" by Terry Robinson, CC BY-SA 2.0.

The gain

In a three-experiment study (n = 263, 293 and 367), assistant-style AI recommendations produced higher adoption intentions for search goods and friend-style recommendations produced higher adoption intentions for experience goods.

In brief

Study 2 (n = 293) traced the pathways: assistant-type advice raised adoption of search goods mainly through cognitive trust, and friend-type advice raised adoption of experience goods mainly through affective trust.

Study 3 (n = 367) tested message framing. Loss framing strengthened the assistant and search-good pairing, and gain framing strengthened the friend and experience-good pairing. The authors suggest e-commerce firms align AI roles and framing with how easily a product can be evaluated.

Main points

  1. Study 1 (n = 263) found a role and product fit: assistant-type recommendations raised adoption intentions for search goods.
  2. In Study 1, friend-type recommendations raised adoption intentions for experience goods.
  3. Study 2 (n = 293) found that assistant-type advice worked mainly through cognitive trust for search goods.
  4. Study 2 found that friend-type advice worked mainly through affective trust for experience goods.
  5. Study 3 (n = 367) found loss framing strengthened the assistant and search-good fit, while gain framing strengthened the friend and experience-good fit.
  6. The authors describe role plasticity as the same AI system's capacity to enact different socially meaningful roles.

The full story

The work draws on role theory and trust theory, with product evaluability and dual-process logic used to specify when each role should work. It looks at how recommender role interacts with product type and message framing.

Search goods are products whose quality can be judged before purchase, and experience goods are judged through use. Assistant-style advice matched the first kind and friend-style advice matched the second, according to the three experiments.

The authors conclude that role-contingent trust and context-dependent message processing explain how the same AI system persuades differently, and they offer guidance on pairing roles and framing strategies in e-commerce.

Sources

1 on file
  1. Journal of Consumer Behaviourdoi:10.1002/cb.70243
    Peer-reviewed2026-09-03

Talk it over

Four questions to start onWho gets this benefit, and who was left out of the study or story?

Start with a question

Three experiments find AI shopping advice works best when its role fits the producthttps://truvace.com/item/emotional-resonance-or-rational-guidance-exploring-the-mechanisms-through-which-

  1. Who gets this benefit, and who was left out of the study or story?

    The gain In a three-experiment study (n = 263, 293 and 367), assistant-style AI recommendations produced higher adoption intentions for search goods and friend-style recommendations produced higher adoption intentions for experience goods.

    Answer in the thread

  2. How do we know? What can this kind of evidence show, and what can’t it?

    The evidence This rests on a controlled comparison, from Journal of Consumer Behaviour.

    Answer in the thread

  3. What does it leave open? What else would you want to know before acting on it?

    Answer in the thread

  4. Have you seen AI used this way where you study, work or live? Did it go the same way?

    Answer in the thread

The questions are the same on every record. The details under them are taken from this record as written.

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