This studyproposes a novel methodological framework to model shared automated vehiclesacceptance and user preferences using multi-methodology that involves statedpreferencesurvey with an emergingmethodology, used for the first time in this context, serious games. Advancedeconometric modeling methods including hybrid choice models are applied toimprove our understanding of how automated vehicles may be adopted in differentcountries and how shared automated vehicles may be accepted among different populationsegments in three studies. We model the mode choice between three novelemerging transportation services and identify latent market segments notstudied yet in the context of shared automated vehicles, including ridesharing,car sharing and automated transit, using a hybrid choice model. Discretedistribution is used to capture taste heterogeneity of distinct latent classes.Latent variables, socio-demographics and travel habits inform latent classassignment estimated simultaneously with a discrete choice kernel. The effectsof seating designation in shared rides, not modeled previously in this context,is quantified and discussed. Furthermore, this study presents a state of theart methodology that utilized the data obtained from a serious game to estimatea game based model of the choice between different shared automated vehicleservices. The unique nature of the game based data allows to model how userexperience and social interactions can shape decisions over time.