Shared autonomous shuttles (SASs) could improve the mobility infrastructure in the worlds’ growing cities. This novel service could reduce congestion and improve both mobility and sustainability. To facilitate the implementation of SASs, more research is needed on the psychological aspects of sharing a small, intimate shuttle with strangers. The current study is among the first to use open-ended questions to investigate SAS acceptance. This investigation is based on the Multi-Level Model on Automated Vehicle Acceptance (MAVA). We had 236 participants answer short-form interviews including both open-ended questions and quantitative items. Quantitative data were analyzed using descriptive statistics and correlations, and qualitative data analyzed with directed content analysis. Respondents seem very positive about the proposed new transport service. We found that perceived usefulness, hedonic motivation, trust, and social influence shared large correlations with intentions to use. Other factors such as demographics, technology savviness and use of public transport did not share a linear relationship with intentions to use. Qualitative analysis suggests that, while most people do not mind sharing shuttles with strangers, some could find the social situation deterring. People seem most concerned with availability, effectiveness, travel cost and safety. The reported positive attitudes towards the service seem predicated upon trust in the government regulation and proper testing of the technology, that many think of as immature. Regulation and thorough testing may be paramount in keeping people positive. This study emphasizes the importance of trust and safety to adoption of SAS, while suggesting new factors that need further investigation.

Introduction: Shared automated vehicles (SAVs) could significantly enhance public transport by addressing urban mobility challenges. However, public acceptance of SAVs remains under-studied, particularly regarding how informational factors and individual personality traits influence acceptance. Methods: This study explores SAV acceptance using data from an experimental survey of 1902 respondents across Norway. Participants were randomly presented with different informational conditions about SAV services, manipulating vehicle autonomy (fully autonomous vs. steward onboard), seating orientation (facing direction of travel vs. facing other passengers), and ethnicity of co-passengers. Personality traits from the Five Factor Model (FFM) and Social Dominance Orientation (SDO) were assessed. The General Acceptance Factor (GAF), derived from the Multi-Level Model of Automated Vehicle Acceptance (MAVA), was used as the primary outcome measure. Results: No significant main or interaction effects were found from the experimentally altered information conditions. However, personality traits significantly influenced acceptance. Specifically, higher openness and agreeableness positively predicted SAV acceptance, while higher neuroticism and social dominance orientation negatively predicted acceptance. Discussion: The absence of experimental effects suggests either a limited role of the manipulated factors or insufficiently robust manipulations. Conversely, the substantial impact of personality traits highlights the importance of psychological factors, particularly trust, openness, and social attitudes, in shaping SAV acceptance. These findings emphasize the need for tailored communication strategies to enhance SAV uptake, addressing specific psychological profiles and fostering trust in automation.

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.

The primary aim of this study was to develop an accurate measure of acceptance for shared autonomous vehicles (SAVs) and to assess whether this measure can predict intentions to use SAVs. One leading model for explaining technology uptake is the UTAUT (Unified theory of acceptance and use of technology). This model is extensive and has received numerous suggested extensions and revisions, even being developed into a Multi-Level Model of Autonomous Vehicle Acceptance (MAVA). The challenge is to consolidate a model that effectively measures SAV acceptance and to determine which extensions capture the unique social situation within SAVs. The current study used survey data from 1902 respondents. The sample was split into two: one half underwent a principal component analysis (PCA) and the other half a confirmatory factor analysis (CFA). We found that the 24 items we included were reducible to a single general acceptance factor (GAF), with three additional factors measuring interpersonal security, sociability, and attractivity. The GAF was, by a large margin, the most efficacious predictor of intention to use SAVs. The GAF could be further reduced to as little as two predictors, trust and usefulness, accounting for over 70 % of the variance in intention to use. However, there is also an argument to be made that the other components of SAV acceptance may capture different nuances of the service, particularly relating to the social situation. Interaction terms show differences between genders in their rating of sociability and how this impacts intentions to use SAVs. Our findings carry significant implications for future research in this field. They underscore the pivotal roles of trust and usefulness while corroborating the notion that SAV acceptance is best represented by a single latent component. However, further investigation is warranted to explore individual-level moderating effects on the other components, potentially offering novel insights for the design of future SAV services.

This study investigates acceptance of shared autonomous shuttles (SASs) in a suburban area. A model where contextual variables were mediated through trust in SASs and technology optimism was tested. We examined intentions to use SASs without a steward and the significance of social distancing. Data were collected at the start and end of a 2020–2021 pilot involving 922 and 608 participants respectively, operating at SAE level 3. Findings indicate that trust and technological optimism significantly influence the willingness to use SASs, though contextual variables show minimal impact. Older adults and women displayed lower trust and optimism, reducing their usage intentions. These two groups also feel that it is more important to be able to keep social distance while riding SASs. The study suggests that future pilots should avoid negative impacts from using immature technology and address the social needs of specific groups.