Understanding Efficacy of Literature Retrieval on Robo-advisory in Finance Sector: Exploring Performance Metrics

Abstract

Objective: Aim is to evaluate and compare performance of Scopus  and Web of Science database in retrieving literature for Robo-advisory  in finance sector.  Methodology: Five systematic literature reviews and bibliometric  analysis on the theme Robo-advisory were selected. References of these  5 SLR were considered and a corpus of 137 most relevant documents  were identified. From titles of 137 documents, most commonly  used keywords were identified and search query “Robo-advi*” was  formulated. Precision, Recall and F1 measure were calculated after  executing the query on Scopus and Web of Science databases.  Results: Higher recall of 75.2% was exhibited for the query by Scopus  as compared to 34.31% by Web of Science. Thus, Scopus is more  effective in capturing relevant literature on the theme. The precision  of query executed on Scopus was 65.71% as compared to 61.98% in  Web of Science. Thus, implying that a large proportion of information  retrieved from Scopus is relevant to search query thereby indicating  a higher level of accuracy by Scopus. From the results of F1 score,  Scopus has a better balance between precision and recall. Thereby  concluding that Scopus is more effective in information retrieval as it  retrieves lesser number of irrelevant documents.  Contribution: It offers valuable insights into the effectiveness of  information retrieval from these databases on the theme under study.  Researchers can make more informed decisions about selecting  database for literature review and bibliometric analysis.