Exploring intention to use cryptocurrencies payment across age groups and gender: a multi-stage machine learning application on the extended UTAUT model

Given the increasingly widespread use of smartphones for cashless payment, the potential for using cryptocurrencies as an alternative e-payment asset becomes attractive. Promoting blockchain-based cryptocurrencies for e-payment faces challenges in payment conversion latency, where hard-earned crypto coins are not a recognised asset for actual payment. This study investigates users’ intention to use cryptocurrencies in daily e-payment transactions. A multi-stage artificial intelligence-based analysis pipeline was employed to identify key factors of crypto-based e-payment usage and model the interaction between these factors. Experiments were conducted based on different target criteria, and factors corresponding to these target criteria were evaluated. Results show that platform reliability, improved usefulness, user-friendliness, and self-efficacy were directly associated with users’ intention to use crypto coins for e-payment services. Age and gender differences were also evaluated across factors affecting users’ intentions to use the new technology. Implications for management and cryptocurrency coin providers were discussed.