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Robo-advisors: a futuristic approach to wealth management

Chanchal Phore et al.

International Journal of Services Operations and Informatics2025https://doi.org/10.1504/ijsoi.2025.152353article
AJG 1
Weight
0.50

What the paper says

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.

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https://doi.org/https://doi.org/10.1504/ijsoi.2025.152353

Or copy a formatted citation

@article{chanchal2025,
  title        = {{Robo-advisors: a futuristic approach to wealth management}},
  author       = {Chanchal Phore et al.},
  journal      = {International Journal of Services Operations and Informatics},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/ijsoi.2025.152353},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.