The Role of Trust and Attitudes in the Acceptance of Artificial Intelligence: A Meta-Analytic Structural Equation Model, submitted version

Artificial intelligence (AI) has been identified as a technological revolution for the next decade (Kelly et al., 2023), with the potential to improve human life in several domains. The present study analyses the factors influencing AI acceptance in adults. A quantitative synthesis based on Kelly et al. (2023) was conducted using one-stage meta-analytic structural equation modeling (OSMASEM; Jak et al., 2021).A total of 40 studies comprising 36,403 subjects were included, yielding 43 correlation matrices and 110 correlation coefficients. The OSMASEM results support a dual-pathway model to AI acceptance: perceived usefulness (PU) is a strong predictor of trust in AI (TR) (β31 = .75, p < .001), which in turn predicts behavioral intention (BI) (β53 = .43, p < .001). Concurrently, performance expectancy (PE) predicts attitudes towards AI (ATT) (β42 = .75, p < .001), which also predicts BI (β54 = .40, p < .001). TR and ATT mediate positive relationships between PU (β51 = .32, p < .001) and BI, and between PE (β52 = .30, p < .001) and BI.This review contributes to understanding the factors influencing AI acceptance. In practical terms, these findings emphasize the importance of internal factors such as TR and ATT, alongside conventional acceptance factors (PU, PE). The need for further research in this area is highlighted, with the recommendation to employ specific AI acceptance models.These results suggest that while core cognitive evaluations (PU, PE) remain crucial, their influence is channeled through more proximal affective and relational constructs (TR, ATT) indicating a potential evolution from traditional technology acceptance frameworks when applied to AI.