Team Performance and Adaptability in Crisis Management: A comparison of cross-functional and functional teams
Bibliographic record
Abstract
Crisis management (CM) is a facet of command and control (C2) characterized by complexity and uncertainty, in addition to high time pressure. In order to meet the challenges of this kind of unpredictable crisis situations, teams must be able to adapt and coordinate in an effective way. The functional structure (i.e., each team member is allocated a unique functional role) is the most common in CM, but it is not necessarily the most efficient one. Structures that encourage independence and flexibility, like the cross-functional structure (i.e., team functions are shared across all team members), could promote much better performance in this kind of situations. We compared these two structures, functional and cross-functional, in a dynamic situation of CM. C 3 Fire, a forest firefighting simulation, was used to compare team structure on the basis of performance (process gain), communication, coordination and adaptability. Cross-functional team structures presented a better process gain, a more efficient coordination, and less communication. Surprisingly, no differences were seen regarding adaptability.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".