The Evolution of Robo-advisor: So far, a Bibliometric Analysis of Adoption and Pattern.

Abstract

Introduction: This article attempts to present a comprehensive knowledge map and conceptual framework about the development of robo advisors in retail investor wealth management. It examines a nine-year dataset gathered from the databases of ProQuest, Enesco, Web of Science, and Scopus, where the data were collected and examined following the SPAR-4-SLR procedure. The publications published between 2014 and 2025 were examined using bibliometric analysis and a systematic literature review strategy, employing a suite of software programs, including VOSViewer and Bibliography R.

Objectives: This study aims to provide a thorough conceptual framework and knowledge map about the evolution of robo-advisers in retail investor wealth management. It addresses the research question, such as: What is the trend of publications on robo-advisors?

What are the top journals, articles, authors, countries, and institutions for robo-advisor research? What are the knowledge clusters in the intellectual structure of a robo-advisor?

And: What future opportunities are there in robo-advisor research

Methods: This Bibliometric review paper utilizes technology to collect information from online scientific databases, including ProQuest, Web of Science, EBSCO, and Scopus. Specialized programs, including Microsoft Excel, VOSviewer, and Bibliometrix-R, were utilized to analyze the data further. To ensure transparency and repeatability of the study, the systematic literature review for this topic is conducted by the SPAR-4-SLR review protocol (Paul et al., 2021).

Results: The publishing pattern shows a consistent increase in research on robo-advisors. The pattern shows that although a study on robo-advisors existed before the COVID-19 pandemic, the sharp rise in 2020–2021 indicates that market disruptions brought on by the pandemic and the increase in digital financial services significantly boosted scholarly interest. The field has attained permanence, rather than being a passing research trend, as evidenced by the consistent publication of at least four annual papers beyond 2020. The small number of prestigious publications, however, suggests that there are numerous opportunities for excellent contributions.

Furthermore, transparency and social media play important roles, and trust emerges as a critical predictor of adoption, particularly among young retail investors. Market heterogeneity is highlighted by the mixed performance findings, which suggest that robo-advisors are more advantageous for less diversified investors than for more diversified ones. Research prospects for larger research communities and cross-cultural validation are revealed by the focus on a small number of prolific authors and particular geographic markets (India, China, Germany, and the US).

Conclusions: In conclusion, this  analysis of robo-advisors showa how they affect the behaviour of retail investors. It highlights the important role they play in supplying financial data and assisting with well-informed decision-making. Numerous benefits are provided by robo-advisors, such as reduced fees, automated portfolio management, and easier accessibility. There is conflicting evidence regarding the effectiveness of robo-advisors versus traditional investment strategies. When deciding between robo-consultants and traditional financial advisors, it is crucial to take into account personal needs and financial risk tolerance. Even though technology has revolutionised retail investing, much more has to be discovered about how these developments affect investor behaviour. Future studies should look into how robo-advisors' technical advancements might be used to help retail clients make better investing choices while reducing the