When and Why Employees Can (Cannot) Learn from Their Errors: From a Cognitive Rumination Perspective
Bibliographic record
Abstract
Treating errors as workplace goal-nonattainment, and applying the cognitive theories of rumination, we investigate how employees’ daily error commission may affect their learning outcomes (i.e., next-day task efficacy) through two after-work ruminative processes: maladaptive rumination (i.e., affective rumination) and adaptive rumination (i.e., problem-solving pondering). Furthermore, we posit that the effect of daily error commission on these ruminative processes depends on the extent to which individuals consider their errors as goal-nonattainment, which we argue will be affected by error controllability and perceived error management climate. In a 10-day experience sampling investigation of 109 employees (N = 1,090), we found that daily error commission is negatively related to next-day task efficacy via affective rumination, and is positively related to next-day task efficacy via problem-solving pondering. Furthermore, we found both error controllability and perceived error management climate mitigate the negative link between error commission and next-day task efficacy via affective rumination, such that the negative indirect relationship was weaker under either higher error controllability or higher error management climate. Error controllability was also found to mitigate the positive link between error commission and next-day task efficacy via problem-solving pondering, such that the positive indirect relationship was weaker under higher error controllability. Our study contributes to theory and practice by elucidating the processes and outcomes related to individuals’ learning from errors 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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".