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Not all AI is created equal: A meta-analysis revealing drivers of AI resistance across markets, methods, and time
- Truvace record
- TRV-2026-2750raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Systematic review or meta-analysis
- Sources
- 1 sourcePeer-reviewed
- sha256
- a13cb573486f…check it
While artificial intelligence (AI) is used by billions of consumers daily through tools like ChatGPT, prior research often documents that consumers are resistant to it. The current research proposes that such resistance is strongly context-dependent, rapidly evolving, and often an artifact of how researchers study it. We provide a comprehensive synthesis of consumer responses to AI by analyzing 440 effect sizes from 76,142 unique participants across two decades of experimental research. Our meta-analysis reveals…
The problem
A meta-analysis of 440 effect sizes from 76,142 participants found consumers responded more negatively to AI than to humans doing the same task (Cohen's d = -0.21), and far more negatively to robots (d = -0.83).
What this doesn’t fix
The studies differ widely (I2 = 96.15%, effects from -2.43 to 1.66), so the single pooled effect of d = -0.21 describes an average rather than what any one AI product or setting will produce.
Sources
1 on file- International Journal of Research in Marketingdoi:10.1016/j.ijresmar.2025.02.005
What the review found when it combined the studies
These are the review’s own pooled results, copied from its abstract. Truvace did not recalculate them.
Overall consumer responses to AI compared with humans across experimental studies
Cohen's d -0.21 (95% CI -0.29 to −0.14)
72 studies · 76,142 people
The review’s own words
As illustrated in Fig. 3, a multi-level random effects model confirmed a statistically significant overall negative effect (Cohen’s d = -0.21, 95 % CI[-0.29, −0.14], t(171) = 5.51, p < 0.001).
Consumer responses to AI after removing influential outliers
Cohen's d -0.20 (95% CI -0.26 to −0.13)
72 studies · 76,142 people
The review’s own words
(d = -0.20, 95 % CI [-0.26, −0.13], t(168) = 5.82, p < 0.001)
Code checks every number above against the review’s text before it is shown. The short labels are an AI model’s reading of that text, so the review’s own sentence sits under each one. A pooled figure is only as strong as the studies behind it. How Truvace uses pooled results
Cite this record
v1Truvace Impact Record TRV-2026-2750, v1: “Not all AI is created equal: A meta-analysis revealing drivers of AI resistance across markets, methods, and time.” Truvace, 2026-11-12. /record/TRV-2026-2750 (accessed on the day you copy it). sha256 a13cb573486f0ce3…
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