Understanding factors influencing user retention in shared e-scooter schemes: A comparative study of the UK/EU and the US

• Machine learning models can successfully predict shared e-scooter user retention. • Perceived utility and user-friendliness are key drivers of user retention. • Green awareness and individual values affect retention differently in UK/EU and US. • Insights guide tailored development of shared e-scooter service across regions. Shared e-scooters are increasingly recognised as a sustainable travel mode for tackling urban transportation challenges, including congestion and air pollution. Their early adoption in U.S. cities, followed by expansion across Europe and ongoing trials in the UK, underscores the need to understand key factors for the scheme’s success and development, particularly regarding the acceptance and retention of existing users. This study adopts an explainable machine learning approach to predict and understand the likelihood of continued shared e-scooter usage among current users. It provides insights into the important factors influencing user retention, while also comparing the UK/EU and US contexts. The developed models exhibit high predictive accuracy and highlight the perceived utility and user-friendliness of e-scooters as key determinants influencing users’ willingness for future use. The research sheds light on psychographic characteristics, emphasising the impact of green awareness and individual’s values on users’ retention, and how their importance may vary between the UK/EU and US users. The findings are useful for guiding future strategic development of shared e-scooter services in different geographic contexts.