The Effect of Humanizing <scp>Robo‐Advisors</scp> on Investor Judgments*
Bibliographic record
Abstract
ABSTRACT We examine the effect of humanizing (naming) robo‐advisors on investor judgments, which has taken on increased importance as robo‐advisors have become increasingly common and there is currently little SEC regulation governing key aspects of their use. In our first experiment, we predict and find that investors are more likely to rely on the investment recommendation of an unnamed robo‐advisor, whereas they are more likely to rely on the investment recommendation of a named human advisor. Theory suggests one reason that naming a robo‐advisor may have drawbacks pertains to the complexity of the task the robo‐advisor performs. We explore the importance of task complexity in our second experiment. We predict and find that investors are less likely to rely on a named robo‐advisor when the advisor is perceived to be performing a relatively complex task, consistent with our first experiment, and more likely to rely on a named robo‐advisor when the advisor is perceived to be performing a relatively simple task, consistent with prior research on human‐computer interactions. Our findings contribute to the literature examining how technology influences the acquisition and use of financial information and the general literature on human‐computer interactions. Our study also addresses a call by the SEC to learn more about robo‐advisors. Lastly, our study has practical implications for wealth management firms by demonstrating the potentially negative effects of making robo‐advisors more humanlike in an attempt to engage and attract users.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".