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Virtual Team Leadership

2009· book-chapter· en· W4238140676 on OpenAlexaff
Laura Hambley, Tom O’Neill, Theresa J. B. Kline

Bibliographic record

VenueAdvances in e-collaboration series/Advances in e-collaboration (AECOB) book series · 2009
Typebook-chapter
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsWestern UniversityUniversity of Calgary
Fundersnot available
KeywordsTeamworkVirtual teamPsychologyQualitative researchKnowledge managementComputer sciencePolitical scienceSociology

Abstract

fetched live from OpenAlex

The purpose of this study was to improve the understanding of virtual team leadership occurring within existing virtual teams in a range of organizations. Qualitative data were collected through comprehensive interviews with nine virtual team leaders and members from six different organizations. A semi-structured interview format was used to elicit extensive information about effective and ineffective virtual team leadership behaviours. Content analysis was used to code the interview transcripts and detailed notes obtained from these interviews. Two independent raters categorized results into themes and sub-themes. These results provide real-world examples and recommendations above and beyond what can be learned from simulated laboratory experiments. The four most important overarching findings are described using the following headings: 1) Leadership critical in virtual teams, 2) Virtual team meeting effectiveness, 3) Personalizing virtual teamwork, and 4) Learning to effectively use different media. These findings represent the most significant and pertinent results from this qualitative data and provide direction for future research, as well as practical recommendations for leaders and members of virtual teams.

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 categoriesMeta-epidemiology (narrow), Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.957
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.027
Open science0.0010.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.015
GPT teacher head0.298
Teacher spread0.283 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2009
Admission routes1
Has abstractyes

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