Antecedents of Disaster Preparation: What Factors Lead Firms to Prepare for Natural Hazards?
Bibliographic record
Abstract
In a largely exploratory study, we investigate: 1) which factors affect how managers identify salient exogenous hazards?, and 2) what are the antecedents of firm preparation for these hazards (e.g., natural disaster risk)? Our analysis is conducted using a two part approach. Both parts are based on an international survey of 575 managers across 18 disaster-prone countries. The first part of the study focuses on examining the factors that affect managers¡¯ identification of natural disaster risk. We find that experience with natural disasters and the locational natural hazard risk where a firm is located significantly affects managers¡¯ identification of firm-specific natural disaster risk as an important firm issue. In the second part we look at the potential antecedents of firm preparedness for exogenous disasters. Our results show that learning and collaborative capabilities are positively related to preparation for natural disasters but self-solving approaches are not. Organizations that expect to go-it-alone are expected to face greater risk in the event of a natural disaster.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".