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Record W2314116303 · doi:10.1177/154193121005401953

Team Performance and Adaptability in Crisis Management: A comparison of cross-functional and functional teams

2010· article· en· W2314116303 on OpenAlexaff
Geneviève Dubé, Tremblay Sébastien, Banbury Simon, Vincent Rousseau

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2010
Typearticle
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsUniversité de MontréalUniversité Laval
Fundersnot available
KeywordsAdaptabilityFlexibility (engineering)Process (computing)Computer scienceProcess managementRisk analysis (engineering)Independence (probability theory)Knowledge managementBusinessMathematicsManagement

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.530

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.279
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2010
Admission routes1
Has abstractyes

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