<i>Retracted:</i> Decision Aid Reliance: A Longitudinal Field Study Involving Professional Buy‐Side Financial Analysts*
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
This study complements and extends prior decision aid (DA) research by examining the DA reliance behavior of professional buy-side financial analysts in the context of their actual work environment. A large mutual fund company provided data on buy-side analysts' earnings forecasts over the course of one year, during which forecasts were made at the end of each quarter for the following four consecutive quarters. As part of the decision process, all analysts could voluntarily access a DA to assist them in forecasting earnings. Consistent with extant DA theory, the results indicate that analysts with greater performance-contingent incentives were less likely to rely on the DA and analysts with more complex portfolios were more likely to rely on the DA. Contrary to the results of most DA research and inconsistent with extant DA theory, analysts with greater task ability relied more on the DA than analysts with lesser ability. Finally, when DA reliance was high, analysts' forecast accuracy was also high, regardless of DA accuracy. The results provide valuable insight into the use of DAs by professional decision makers and the influence of DA reliance on their judgments in light of real-world pressures and performance consequences. The theoretical and practical implications of this study call for more research into why, how, and under what conditions highly skilled knowledge workers rely on the advice of DAs.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".