Mapping the Artificial Intelligence–Investor Decision-Making Nexus: A Comprehensive Bibliometric and Science-Mapping Analysis of Scopus- Indexed Research

Abstract

AI is transforming the methods that both individual and institutional investors use to search for information, assess risks, and deploy capital. However, the academic literature on the links between artificial intelligence and investor decision-making is still inconsistent and divided across different disciplines and institutions. This paper aims to provide a comprehensive bibliometric and science mapping analysis of the relationship between artificial intelligence and investor decision-making. The data was compiled from 134 articles published in peer-reviewed journals from 2016 to 2026 selected from Scopus via the following search query in Scopus the search query used was: TITLE-ABS-KEY ("Artificial Intelligence" OR "AI") AND TITLE-ABS-KEY ("Investor Decision-Making"). The search was restricted to publications classified under the fields of Business, Management and Accounting, or Econometrics and Finance.

This analysis was made through Bibliometric/Bibliophagy based on R and VOS viewer for bibliometric analysis and mapping. Results of the study point to an annual growth rate of 44.97% with the awaited output concentrated after 2023The countries with the most authorship are India (73) and China (65) while China (17) and India (16) are the most productive correspondents. As for the sources, the most notable were Sustainability The analysis includes publications from the International Journal of Financial Studies (IJFS) and the Journal of Risk and Financial Management (JRFM). The highest-cited article globally is Hájek and Henriques (2017, Knowledge-Based Systems) that has been cited 297 times. Shanmuganathan (2020, Journal of Behavioural and Experimental Finance) comes second with 174 citations. According to thematic mapping, "decision making" and "decisions makings" become the Motor themes of the research, "data mining", "cost–benefit analysis" and "explainable AI" are identified as Niche themes and "alternative energy", "consumer behaviour", "governance approach" and "energy efficiency" come under Emerging or Declining themes. Using the co-occurrence network (VOS viewer), the study identified three main clusters: (1) "decision-making" and "artificial intelligence", (2) "electronic trading", "commerce" and "decision-support infrastructure" and (3) "risk management", "big data" and "sustainable development". The paper finishes with a discussion of the relevant, yet unexplored areas, namely explainability, behavioural biases mitigation and regional case studies of emerging markets, while providing recommendations for the future studies aimed at researching AI and investor decision making.