A new framework to predict and visualize technology acceptance: A case study of shared autonomous vehicles

Public acceptance is critical to the adoption of Shared Autonomous Vehicles (SAVs) in the transport sector. Traditional acceptance models, primarily reliant on Structural Equation Modeling, may not adequately capture the complex, non-linear relationships among factors influencing technology acceptance and often have limited predictive capabilities. This paper introduces a framework that combines Machine Learning techniques with chord diagram visualizations to analyze and predict public acceptance of technologies. Using SAV acceptance as a case study, we applied a Random Forest machine learning approach to model the non-linear relationships among psychological factors influencing acceptance. Chord diagrams were then employed to provide an intuitive visualization of the relative importance and interplay of these factors at both factor and item levels in a single plot. Our findings identified Attitude as the primary predictor of SAV usage intention, followed by Perceived Risk, Perceived Usefulness, Trust, and Perceived Ease of Use. The framework also reveals divergent perceptions between SAV adopters and non-adopters, providing insights for tailored strategies to enhance SAV acceptance. This study contributes a data-driven perspective to the technology acceptance discourse, demonstrating the efficacy of integrating predictive modeling with visual analytics to understand the relative importance of factors in predicting public acceptance of emerging technologies. • An ML-chord diagram framework to predict and visualize technology acceptance. • Analyzed shared autonomous vehicle (SAV) acceptance at both factor and item levels. • Identified attitude towards SAVs as urban additions as the top usage predictor. • Revealed differences in SAV adopters and non-adopters for tailored acceptance aims. • Integrated predictive modeling and visual analytics for acceptance studies.