The rapid growth of robo-advisory services has sparked increasing academic interest, yet a comprehensive understanding of the intellectual landscape remains underdeveloped. Hence, the present study conducts a bibliometric analysis using performance analysis and science mapping techniques. By applying bibliographic coupling, key research themes are identified and structured using the antecedents-decisions-outcomes (ADO) framework to provide conceptual clarity. The analysis reveals six major themes: Anthropomorphism and Intention to Adopt Robo-Advisors and Driving Forces behind the Emergence and Advancement of Robo-Advisors (Antecedents); Robo-Advisors as Expounding Financial Decision Makers and Functionality and Challenges of Robo-Advisors (Decisions); and Models and Frameworks for Enhancing Investment Performance (Outcomes). This study contributes to literature by mapping the evolution of research in robo-advisory services, identifying thematic clusters, and proposing future research directions. Specifically, it highlights the potential for integrating robo-advisors into ESG-frameworks, exploring post usage investor behaviour, enhancing customer loyalty, and incorporating NLP to reduce behavioural biases in financial decision-making.
Phore et al. (Wed,) studied this question.