The Double‐Edged Sword of Error Sharing in Organizations: From A Self‐Disclosure Perspective
Bibliographic record
Abstract
Abstract Extant research highlights the importance of error sharing for managing errors in organizations, but little work examines what happens to employees who disclose errors. Treating errors as sensitive information, we draw on the self‐disclosure literature to propose that error sharing can influence leaders’ evaluations of employee ability and integrity, which affect leader trust in the employee; error visibility and severity work as contingency factors in the above links. We conducted two field studies and one experimental study to test our hypotheses. We used data collected in China from manufacturing companies (560 employees from 71 teams in Study 1), a high‐reliability organization (359 employees from 104 teams in Study 2), and an online sample (356 participants in Study 3). Results show that error sharing impairs leader trust via the negative evaluation of the employee's ability but enhances trust via the positive evaluation of the employee's integrity; error visibility and severity moderate the relationships between error sharing and leader evaluation of employee integrity and leader trust such that the positive relationships are enhanced when errors are of lower visibility or higher severity. Our study offers a novel perspective to understand the relational consequences of error sharing at work.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".