A Bibliometric Exploration of the Financial Robo-Advisory Landscape (IJARSAI International Journal of Advanced Research Scientific Analysis & Inferences)

Summary

• Annual scholarly production reached its peak in 2025 with approximately 45 articles.p.4
• The study by Gomber, P. (2018) is the most globally cited document in the dataset, accumulating 985 citations.p.5
• Rossi A G is the top contributor by volume with 5 publications, followed by D’Acunto F with 4 publications.p.5
• Maastricht University, Seoul National University, and University of Zaragoza are the leading institutional affiliations with 6 publications each.p.6
• Finance Research Letters represents the most productive journal in the domain with 9 publications.p.6
• China and the United States lead country scientific output with 74 and 62 publications respectively.p.7

Key insights

1. Academic publication output on robo-advisors remained low from 2015 to 2017 before accelerating significantly from 2018 onward.p.4
2. Average citation rates per year peaked following key early publications in 2018 and declined subsequently because recent papers have had less time to gather citations.p.4
3. Foundational papers published in leading journals such as Journal of Management Information Systems and Review of Financial Studies represent the majority of accumulated citations.p.5
4. Global research production on robo-advisors is distributed across multiple nations but dominated by China and the United States.p.7
5. Keyword co-occurrence analysis identifies robo advisor, fintech, trust, and artificial intelligence as the most central terms in the literature.p.8
6. Research themes have transitioned from early technical and portfolio selection topics toward user trust, adoption, and decision-making behavior.p.8
7. Motor themes driving current academic research focus heavily on user trust, adoption factors, and digital financial advice impact.p.9
8. Methodological approaches in niche themes frequently rely on the expectation-confirmation model and structural equation modelling to assess technology acceptance.p.9