A simple model for complex technology: introducing and testing a framework to understand acceptance of shared automated vehicles

Abstract Shared automated vehicles (SAVs) may transform urban mobility but face strong public resistance. Existing acceptance research is fragmented and often relies on complex frameworks. We introduce a simplified shared automated vehicle acceptance (SAVA) model, identifying trust, utility and social comfort as core predictors of SAV acceptance. Using structural equation modelling, we tested whether these factors form a general acceptance factor (GAF) or operate as distinct but correlated predictors of intention to use. A 2 × 2 × 2 experimental design further assessed whether targeted informational interventions could increase trust, utility and social comfort. Monte Carlo-based power analyses indicated a minimum of 800 participants to detect small effects (Cohen’s f = 0.20) with 80% power at α = 0.05; we recruited 1250 respondents after data cleaning, ensuring adequate power. Results show that trust, utility and social comfort are best modelled as distinct but correlated constructs, implying rather than establishing a GAF. Utility exerted the strongest effect. Experimental manipulations had no significant impact, suggesting stronger interventions are needed to shift acceptance. The SAVA model provides a parsimonious, testable framework explaining intention to use SAVs. This recommended registered report advances theory and offers practical insights for policymakers and providers seeking to improve SAV acceptance.