Community resilience to natural disasters: the role of disaster entrepreneurship
Bibliographic record
Abstract
Purpose The paper aims to examine the conditions under which disaster entrepreneurship contributes to community-level resilience. The authors define disaster entrepreneurship as attempts by the private sector to create or maintain value during and in the immediate aftermath of a natural disaster by taking advantage of business opportunities and providing goods and services required by community stakeholders. Design/methodology/approach This paper builds a typology of disaster entrepreneurial responses by drawing on the dimensions of structural expansion and role change. The authors use illustrative case examples to conceptualize how these responses improve community resilience by filling critical resource voids in the aftermath of natural disasters. Findings The typology identifies four different disaster entrepreneurship approaches: entrepreneurial business continuity, scaling of organizational response through activating latent structures, improvising and emergence. The authors formulate proposition regarding how each of the approaches is related to community-level resilience. Practical implications While disaster entrepreneurship can offer for-profit opportunities for engaging in community-wide disaster response and recovery efforts, firms should carefully consider the financial, legal, reputational and organizational implications of disaster entrepreneurship. Social implications Communities should consider how best to harness disaster entrepreneurship in designing their disaster response strategies. Originality/value This research offers a novel typology to explore the role that for-profit firms play in disaster contexts and adds to prior research which has mostly focused on government agencies, non-governmental organizations and emergency personnel.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".