AI aversion or appreciation? A capability-personalization framework and a meta-analytic review.

Abstract

Artificial intelligence (AI) is transforming human life. While some studies find that people prefer humans over AI (AI aversion), others find the opposite (AI appreciation). To reconcile these conflicting findings, we introduce the Capability-Personalization Framework. This theoretical framework posits that when deciding between AI and humans in a context, individuals focus on two dimensions: (a) perceived capability of AI and (b) perceived necessity for personalization. We propose that AI appreciation occurs when (a) AI is perceived as more capable than humans and (b) personalization is perceived as unnecessary in a given decision context, whereas AI aversion occurs when these conditions are not met. Our Capability-Personalization Framework is substantiated by a meta-analysis of 442 effect sizes from 163 studies (N = 82,078): AI appreciation occurs (d = 0.27, 95% CI [0.17, 0.37]) when AI is perceived as more capable than humans and personalization is perceived as unnecessary in a given decision context; otherwise, AI aversion occurs (d = -0.50, 95% CI [-0.63, -0.37]). Moderation analyses suggest that AI appreciation is more pronounced for tangible robots (vs. intangible algorithms), for attitudinal (vs. behavioral) outcomes, in between-subjects (vs. within-subjects) study designs, and in low unemployment countries, while AI aversion is more pronounced in countries with high levels of education and internet use. Overall, our integrative framework and meta-analysis advance knowledge about AI-human preferences and offer valuable implications for AI developers and users. (PsycInfo Database Record (c) 2025 APA, all rights reserved).