Do “rising stars” avoid risk?: status-based labels and decision making
Bibliographic record
Abstract
Purpose – The purpose of this paper is to empirically examine how status-based labels, based on future capabilities, can impact people's risk tolerance in decision making. Design/methodology/approach – In this paper the authors developed and tested theoretical arguments using a set of three studies employing a scenario-based approach and a total of 449 undergraduate business students. Findings – The findings suggest that labeling people in terms of future capabilities can trigger perceptions of public scrutiny and influence their risk preferences. Specifically, the results reveal that individuals who are recipients of high-status labels tend to choose lower risk decision options compared to their peers. Research limitations/implications – The study employed scenarios to examine the issue of employee labeling. The extent to which these scenarios have truly captured the dynamics of labeling is questionable, and future research should employ a field-based study to examine whether the reported effect can be observed in a “real” work context. Practical implications – Organizations are concerned about their future leadership capacity and often attempt to grow leadership talent by identifying high-potential employees early on. The results of this study suggest that such practice may have an unintentional negative effect of reducing high-potentials’ tolerance toward risky decision making, thus potentially impacting these future leaders’ decision making in the realm of corporate strategy, R&D, etc. Originality/value – The issue of how labeling individuals in terms of future capabilities can impact their risk preference has been largely ignored by organizational research. This paper suggests that the popular practice of identifying high-potential employees may have unintentional negative effects by lowering their risk tolerance.
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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".