Caudal autotomy as a managerial tool in the change processes involved in organizational behavior
Bibliographic record
Abstract
This study uses the metaphor of caudal autotomy - the ability of some animals to shed their tails to evade predators - to investigate the role of strategic flexibility in managing organizational change. The research explores the concept of 'organizational autotomy', where parts of a company are selectively detached to enhance overall survival and adaptability amidst turbulent market environments. We examine diverse organizations and their responses to significant industry shifts, economic challenges, or internal crises. This study was based on the studies of El Kadi & Pelekais (2014) and Nelson, Quick, Armstrong, Roubecas, Condie. (2020). The findings indicate that successful 'organizational autotomy' relies on three key factors: Proactive identification of detachable elements, efficient execution of detachment, and a robust regrowth plan for post-detachment sustainability. This paper, thus, presents a novel perspective on organizational adaptability and resilience, providing valuable insights for leaders, managers, and change agents. Future research directions include quantifying the impacts of 'organizational autotomy' and establishing best practice guidelines for its implementation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".