Algorithmic Guidance and Investor Learning: An Empirical Examination of Robo-Advisors (International Journal of Research Publication and Reviews)

Summary

• A total of 123 questionnaires were distributed to students and young investors in Vijayawada, with 94 retained for analysis.p.4
• Among the 94 respondents, 51.1% (48 respondents) reported having used a robo-advisory service.p.5
• The sample was predominantly young, with 73.4% of respondents aged below 25 years.p.5
• Robo-advising adoption significantly predicted digital financial education (B = 0.325, β = .303, p = .003), explaining 9.2% of its variance.p.8
• Inspection of the dataset identified only 28 unique respondent names across the 94 recorded rows, indicating file duplication.p.9

Key insights

1. Robo-advisor usage was significantly associated with gender, with 65.7% of men reporting usage compared to 42.4% of women.p.6
2. Robo-advisor users rated their self-perceived investment performance higher than non-users, with mean construct scores of 3.73 versus 3.28.p.7
3. Adoption of robo-advising services significantly predicted financial sophistication in simple regression (β = .325, p = .001).p.8
4. Digital financial education lost statistical significance as a predictor of financial sophistication when tested jointly with adoption in multiple regression (p = .089).p.8
5. The positive relationship between adoption and sophistication was driven primarily by diversification awareness and rational decision-making rather than analytical knowledge.p.9
6. Re-estimating regression models on the subset of 28 unique respondents rendered the relationship between adoption and digital financial education non-significant (p = .247).p.9