Algorithmic Guidance and Investor Learning: An Empirical Examination of Robo-Advisors

Author

Dasari Sharon, Dr. I. Mohana Krishna


Abstract

 Growth in robo-advisors has broadened retail access to investor-centric digital financial advice; however, it remains uncertain whether using them makes investors smart or feels smarter. This study investigates relationships between robo-adoption, digital financial literacy, financial expertise, and investment results among young and late-career retail investors in Vijayawada, Andhra Pradesh. Data were gathered with a structured, self-administered Likert-scale questionnaire; 94 effectively responded (out of 123 distributed). 

Chi-square tests, one-way ANOVA, and linear regressions were computed. 

Robo-adoption was significantly related to gender (1) = 4.79, p =.029. Adoption, digital literacy, expertise, and outcomes were significantly different across age, gender, education, income, experience, and occupational groups. Simple regressions indicated all four hypotheses are supported: adoption predicted digital literacy ( =.30, p =.003) and expertise ( =.32, p =.001), digital literacy predicted expertise ( =.26, p =.012), and expertise predicted perceived investment success ( =.65, p <.001, R =.43). When adopting and learning were tested jointly, only adoption was significant. 

Sub-dimension analyses indicate that the effect of adoption resides mainly in diversification awareness and information-based decision-making, while the self-perceived risktolerance and rate-of-return knowledge was slightly lower among more frequent users. 

Reliability of the scales for expertise and performance outcomes was weak, and data set contains duplicate respondents, so results should be considered indicative.



Keywords

robo-advising; financial sophistication; retail investors; digital financial education; investment decision-making; India



Full Text:

Download Paper PDF


References


  1. 1. Bianchi, M., & Brière, M. (2024).Human-Robot Interactions in Investment Decisions.Management Science, 72(1), 14–31.

Management Science

  1. D’Acunto, F., Prabhala, N., & Rossi, A. G. (2019).The Promises and Pitfalls of Robo-Advising. The Review of Financial Studies, 32(5), 1983–2020.

Oxford Academic

  1. D’Acunto, F., & Rossi, A. G. (2023).Robo-Advice: Transforming Households into Rational Economic Agents. Annual Review of Financial Economics, 15, 543–563.

Annual Reviews

  1. Reher, M., & Sokolinski, S. (2024).Robo Advisors and Access to Wealth Management. Journal of Financial Economics, 155, Article 103829.

ScienceDirect

+1

  1. Singh, S., & Kumar, A. (2025).Investing in the Future: An Integrated Model for Analysing User Attitudes Towards Robo-Advisory Services with AI Integration. Vilakshan – XIMB Journal of Management, 22(1), 158–175.

DOAJ

Publication-year note: The journal issue is Volume 22, Issue 1, dated April 2025, while some bibliographic records list the article as 2024. Use the year corresponding to the version you consulted and follow your institution's referencing requirements

  1. 6. Rossi, A. G., & Utkus, S. P. (2020).Who Benefits from Robo-Advising? Evidence from Machine Learning.SSRN working paper.

SSRN

  1. Reher, M., & Sokolinski, S. (2024). Robo advisors and access to wealth management. Journal of Financial Economics, 155, Article 103829. https://doi.org/10.1016/j.jfineco.2024.103829 

8.Van Rooij, M., Lusardi, A., & Alessie, R. (2011). Financial literacy and stock market participation. Journal of Financial Economics, 101(2), 449–472. https://doi.org/10.1016/j.jfineco.2011.03.006 

9.Oehler, A., Horn, M., & Wendt, S. (2022). Investor characteristics and their impact on the decision to use a robo-advisor. Journal of Financial Services Research, 62, 91–125. https://doi.org/10.1007/s10693-021-00367-8 

10.Lusardi, A., & Mitchell, O. S. (2014). The economic importance of financial literacy: Theory and evidence. Journal of Economic Literature, 52(1), 5–44. https://doi.org/10.1257/jel.52.1.5 

 

 

 

 

 

Share your valuable work from Social Media Buttons