The emerging field of Robo Advisor: A relational analysis

Abstract

Robo advising is an emerging business model that aims to popularize investment advisory services by automating all the activities involved. The robo-advisor (RA) stream of research is relatively new, yet it has started to attract the attention of numerous scholars. This manuscript aims to analyze the academic literature on RA based on its impact on the different phases that make up the investment advice process. Our aim is to investigate which phases of the investment advice service prove of most interest to scholars. Since selecting relevant and precise keywords increases the visibility, accessibility, and indexing of a research paper, we explore the RA research landscape using keyword relational analysis. We analyze metadata from 195 identified papers combining the Scopus and Web of Science databases until May 2022. The analysis unfolds in two parts. First, we examine the relationships and associations of keywords in titles, abstracts, and author keywords to extract knowledge patterns from said associations. As a novelty, a specific dictionary of RA-synonyms formed by 19 disjoint families of concepts is obtained. The second part presents the RA's conceptual and relational structure. For this, five clusters are determined to identify and organize those studies dealing with similar topics. The results of our study suggest that research is more interested in aspects related to the human factor, namely, those with phases requiring more direct contact with the client or investor. The most relevant research interests include the automation of client profiling functions and client response to the latter automation.

Keywords

• G20
• G11
• O33
• G10
• G21
• G24