The evolution of robo-advisors in FinTech: a global bibliometric and thematic analysis, review and future agenda

This study presents a longitudinal bibliometric and thematic analysis of the global robo-advisory research landscape from 2017 to 2026, using a total of 384 peer-reviewed publications selected from an initial dataset of 865 records retrieved from Web of Science and Scopus databases. By employing co-citation, co-authorship, bibliographic coupling, keyword co-occurrence, cluster, thematic, evolutionary, and triangulation analysis, the study attempts to highlight the structural and conceptual evolution of the field. The co-citation analysis shows that robo-advisor research sits at the crossroads of finance, marketing, and information systems, built on foundational work like TAM, UTAUT, SEM, and Markowitz’s portfolio theory; the co-authorship network reveals a diverse field dominated by small, internally cohesive teams, with limited cross-border collaboration, despite the citations that show substantial convergence. Bibliographic coupling shows the central presence of a trust- and intention-focused core, supported by broad adoption and behavioural literature, and a newer wave of extending the field outward into more outcome-based peripheries. The keyword co-occurrence, cluster, thematic, evolutionary, and triangulation analysis differentiates well-known themes from accelerating ones. AI, trust, LLMs, UTAUT, perceived risk and value, anthropomorphism, GenAI, and DeFi remain robust across temporal specifications. The study concludes by providing actionable implications for researchers, practitioners, and policymakers with a call for future research by converting the findings into four research programs: adaptive and personalized investment intelligence, human–AI collaborative advisory, responsible and autonomous financial AI, and programmable and expanded investment ecosystems to ensure responsible and inclusive development of robo-advisory systems across diverse financial ecosystems.