aceA meta-analysis of the effects of AI agent implementation on customer-level outcomes
Firms increasingly use AI agents, such as recommendation algorithms and chatbots, to enhance customer value, yet research documents mixed effects on customer outcomes. To address and clarify these heterogeneous findings, we conduct a meta -analysis of 468 effect sizes reported in 95 articles with 82,751 participants examining AI agent implementation across both substitution contexts (AI replacing humans) and adoption contexts (AI introduced into previously non-AI processes). Results indicate that customers, on a…
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As of the April 4 2026 publication date, a meta-analysis of 468 effect sizes from 95 articles with 82,751 participants examined AI agent implementation across substitution and adoption contexts and found that customers, on average, responded less favorably to AI agent implementation.
The finding matters because firms increasingly use recommendation algorithms and chatbots to enhance customer value, yet the average aversion suggests a prejudicial bias tied to threat and mistrust; uncertainty remains because the direction and magnitude depend on AI design, information, contextual, and cross-country factors.
- Meta-analysis synthesized 468 effect sizes from 95 articles with 82,751 participants
- Analysis distinguished substitution contexts where AI replaces humans and adoption contexts where AI enters previously non-AI processes
- Authors attribute aversion to prejudicial bias explained by implicit prejudice theory
- Aversion linked to perception of threat and mistrust when AI intrudes into human-managed domains
Customers exposed to AI agent implementation in substitution and adoption contexts responded less favorably on average as of the 2026 meta-analysis
The rundown
The analysis covered 468 effect sizes reported in 95 articles with 82,751 participants, comparing substitution contexts described as AI replacing humans and adoption contexts described as AI introduced into previously non-AI processes.
The authors draw on implicit prejudice theory, stating the bias arises when AI systems intrude into domains traditionally managed by humans or manual systems, evoking a perception of threat and mistrust, with examples given as recommendation algorithms and chatbots.
Sources
- Peer-reviewedJournal of Business Research2026-04-04
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The debate