Intention to use Robo-Advisors, considering the Behavioral Reasoning Theory, and moderating effect of prior knowledge and experience.
Bibliographic record
Abstract
Robo-advisors, AI-powered financial services, offer personalized investing solutions but have not achieved the expected adoption rates. This study addresses a critical gap in the literature by examining how the value of openness to change influences the intention to use robo-advisors, through the mediating roles of Reasons for and Reasons Against adoption, within the framework of Behavioral Reasoning Theory (BRT). Additionally, the study explores how financial knowledge and investing experience moderate these mediated relationships in a nonlinear fashion. Data collected from 400 participants through a structured survey was analyzed using Structural Equation Modeling (SEM). The results indicate that while personal values indirectly influence adoption intentions, Reasons For significantly enhance, and Reasons Against impede, the intention to use robo-advisors. The nonlinear moderating effects of financial knowledge and investing experience reveal that the influence of these reasons on intention is most pronounced at moderate levels of these moderators but diminishes at low and high levels. Specifically, financial knowledge strengthens the positive impact of Reasons For and mitigates the negative impact of Reasons Against at moderate levels, while investing experience shows a more complex pattern, amplifying and then weakening these effects. These findings underscore the need for targeted strategies that address both the benefits and perceived barriers to robo-advisor adoption, emphasizing the nuanced role of user knowledge and experience in shaping engagement with AI-driven financial technologies.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".