Financial technologies, specifically robo-advisors, are becoming indispensable in the digital transformation of financial institutions. This study focuses on the effects of user characteristics and the human-like attributes of robo-advisors on downstream outcomes such as trust, financial well-being and loyalty.
Partial least squares structural equation modeling (PLS-SEM) and necessary condition analysis (NCA) was applied.
The findings indicate that (1) human-like characteristics of robo-advisors are essential for fostering users’ trust in robo-advisors; (2) while not necessary conditions, financial self-efficacy and financial socialization positively influence trust; (3) trust, particularly its congruence, communication and commitment dimensions, is key to enhancing financial well-being; (4) the frequency of robo-advisor usage moderates the trust–financial well-being relationship and (5) financial well-being positively impacts customer loyalty.
This research advances robo-advisory service studies by exploring how user traits and humanoid robot features affect trust and loyalty, extending prior research focused on adoption and continuous use. Additionally, the study introduces NCA to pinpoint trust-building factors in robo-advisors, providing practical insights for developing desired consumer behavior (i.e. loyalty) in robo-advisory services.
