Match Me If You Can – Exploring similarity effects in robo-advisor design
(A2026-133294)

AUTHORS

Vanessa Znidaric, Seeburg Castle University; Sarah Wegner, Seeburg Castle University; MAGDALENA BEKK, Seeburg Castle University

ABSTRACT

Robo-advisors are transforming traditional investor profiling by replacing human interactions with algorithmic decision-making. The mechanisms by which consumers adopt these technologies remain underexplored. Extant research indicates that the incorporation of human attributes into robo-advisory systems has the potential to fortify customer confidence and streamline adoption processes. They have mainly focused on gender representation in these systems. This research explores the impact of broader visual similarities, including biological sex and hair traits. Our findings do not support fit theories for designing robo-advisor interfaces. Instead, they suggest that robo-advisors could benefit from reduced anthropomorphism to make them appear more expert and capable of providing financial advice to users. This effect which maybe explained through the uncanny-valley effect due to higher discomfort towards human-like robo-advisors.