Do self-organizing teams promote shared leadership and team performance in crisis management?
Bibliographic record
Abstract
Purpose This study aims to examine how team structure (self-organizing vs functional) influences the emergence of shared leadership and its relationship with team performance in crisis management settings. Design/methodology/approach Forty-eight four-person teams completed a dynamic firefighting simulation (C3Fire) under either self-organizing or functionally assigned roles. Shared leadership was assessed via social network metrics (density and centralization) across four trials, alongside objective measures of team performance. Findings While both team structures exhibited shared leadership, self-organizing teams displayed lower leadership centralization and performed better overall. Leadership density did not predict team performance. Centralization decreased over time in self-organizing teams, which may reflect adaptive leadership emergence. Research limitations/implications The use of a simulated microworld may limit the generalizability of the findings to real-world settings. Future research should explore behavioral indicators of leadership emergence and examine professional teams in real-world crisis contexts. Practical implications Organizations should foster flexible team structures and support role negotiation to enable adaptive and decentralized leadership. Simulation-based training may enhance team responsiveness under crisis conditions. Originality/value This study provides empirical evidence on how structural conditions shape the emergence of shared leadership in dynamic, high-stakes environments. It distinguishes between leadership intensity and distribution, and supports adaptive leadership theory by highlighting the role of structural decentralization and temporal dynamics.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".