The Potential Contribution of the Field of Organizational Development to Crisis Management
Bibliographic record
Abstract
This paper explores the possibilities of cross‐disciplinarity between organization development and crisis management. The departure point of our reflection is that crisis management as a field currently faces two limitations. First, two major trends have characterized this field to date: the sociological analysis of organizational contingencies which focus on disasters as social events and the crisis management planning which emphasizes the development of techniques to master hazards. Despite what we have learned from these approaches, neither seems to lead to a crisis management learning model that fosters organizational resilience in coping with crises. Second, researchers have studied a number of events as single case studies but have not synthesized these case studies. Consequently, each crisis seems idiosyncratic and administrators tend to repeat the same inefficient patterns when a crisis occurs. The research proposal presented in this paper aims to remove these limitations by bringing together two apparently opposing fields of study, that of crisis management, characterized by what are perceived as specific events, and that of organizational development, characterized by the strengthening of organizations' capacities to cope with lasting changes. This paper proposes to explore their potential to work together theoretically and empirically through a research design. We conclude on how this proposal meets the challenges of a new research agenda in the 21st century.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".