Organizational disasters: why they happen and how they may be prevented
Bibliographic record
Abstract
Purpose The purpose of this paper is to look at why organizational disasters happen, and to discuss how organizations can improve their ability to recognize and respond to warning events and conditions before they tailspin into catastrophe. Design/methodology/approach A review of research on organizational disasters suggests that there are a number of information difficulties that can prevent organizations from noticing and acting on warning signals. The paper describes these difficulties using recent examples of organizational mishaps from: 9/11, Enron, Merck Vioxx withdrawal, Barings Bank collapse, Columbia Space Shuttle breakup, and Children's Hospital Boston. Findings The paper identifies three types of information impairments that could lead to organizational disasters: epistemic blind spots, risk denial, and structural impediment. It examines common information and decision practices that make it hard for organizations to see and deal with warning signals. Finally, the paper suggests what individuals, groups, and organizations can do to raise their information vigilance. Originality/value The paper shows that organizational disasters have a structure and dynamic that can be understood, and proposes a number of strategies by which organizations can become better prepared to recognize and contain errors so as to avert disaster.
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| 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".