Arenas-Parra Mar, Rico-Pérez Héctor, Quiroga-Garcia Raquel
Dpt. of Quantitative Economics, University of Oviedo, 33006, Oviedo, Asturias, Spain.
Dpt. of Methodology and Quantitative Analysis, Banca March S.A., C/ Nuñez de Balboa 70, 28006, Madrid, Spain.
Heliyon. 2024 Aug 10;10(16):e35946. doi: 10.1016/j.heliyon.2024.e35946. eCollection 2024 Aug 30.
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.
智能投顾是一种新兴的商业模式，旨在通过自动化所有相关活动来推广投资咨询服务。智能投顾（RA）这一研究领域相对较新，但已开始吸引众多学者的关注。本论文旨在基于其对构成投资建议流程的不同阶段的影响，分析有关智能投顾的学术文献。我们的目的是调查投资建议服务的哪些阶段最受学者关注。由于选择相关且精确的关键词会提高研究论文的可见性、可访问性和索引率，我们使用关键词关系分析来探索智能投顾的研究领域。我们分析了截至2022年5月从Scopus和Web of Science数据库中识别出的195篇论文的元数据。分析分为两个部分。首先，我们检查标题、摘要和作者关键词中的关键词之间的关系和关联，以从这些关联中提取知识模式。一项创新是，我们获得了由19个不相交的概念族构成的智能投顾同义词特定词典。第二部分展示了智能投顾的概念和关系结构。为此，确定了五个聚类，以识别和组织那些处理相似主题的研究。我们的研究结果表明，研究更关注与人为因素相关的方面，即那些在阶段中需要与客户或投资者有更直接接触的方面。最相关的研究兴趣包括客户概况分析功能的自动化以及客户对后者自动化的反应。
