Evolution of Robo‐Advisors: A Literature Review and Future Research Agenda

ABSTRACT Robo‐advisors are algorithm‐driven platforms that automate financial planning and investment management, offering cost‐effective and accessible services with minimal human interaction. Despite their rapid adoption, particularly among retail investors, scholarly research remains fragmented. This study conducts a systematic review of 71 peer‐reviewed articles using the SPAR‐4‐SLR protocol and organises the analysis through the theories, contexts, characteristics, methodologies (TCCM) framework. Findings demonstrate that prominent contributions are published in finance and marketing journals, with the United States and United Kingdom leading global research productivity. Robo‐advisor adoption is primarily driven by attitude and social influence, while behavioural intention, adoption, and continued use are mediated by financial literacy, user perception, emotion, and moderated by trust, demographic characteristics, and income‐to‐investment ratios. This research identifies underexplored theoretical perspectives, including commitment‐trust theory, critical theory of technology and the affect infusion model, particularly from psychological and marketing domains. It further calls for methodological diversification, advocating for quantitative, qualitative and mixed‐method approaches. Emerging research opportunities include novel outcome variables such as consumer vulnerability, resilience and adaptability, especially in response to technological disruption and data breaches. This review advances theoretical development and offers practical insights to support sustained engagement with robo‐advisory services in the post‐adoption phase.