Learning from others’ misfortune: Factors influencing knowledge acquisition to reduce operational risk
Bibliographic record
Abstract
Abstract Risks arising from operations are increasingly being highlighted by managers, customers, and the popular press, particularly related to large‐scale (and usually low‐frequency) losses. If poorly managed, the resulting disruptions in customer service and environmental problems incur enormous recovery costs, prompt large legal liabilities, and damage customer goodwill and brand equity. Yet, despite conventional wisdom that firms should improve their own operations by observing problems that occur in others’ processes, significant operational risks appear to be ignored and similar losses recur. Using a randomized vignette‐based field experiment, we tested the influence of organization‐level factors on knowledge acquisition. Two organization‐level factors, namely perceived operational similarity, and to a lesser extent, market leadership, significantly influenced the risk manager's likelihood of acquiring knowledge about possible causes that triggered another firm's operational loss. These findings suggest that senior managers need to develop organizational systems and training to expand the screening by risk managers to enhance knowledge acquisition. Moreover, industry and trade organizations may have a role in fostering the transfer of knowledge and potential learning from operational losses of firms.
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".