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Record W3216466816 · doi:10.1177/10464964211057116

Unpacking the Role of Feedback in Virtual Team Effectiveness

2021· article· en· W3216466816 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSmall Group Research · 2021
Typearticle
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCornerstoneUnpackingVirtual teamTeam effectivenessPsychologyPsychological interventionKnowledge managementTeam compositionPoint (geometry)Applied psychologyPsychological safetyComputer scienceHuman–computer interactionProcess managementBusiness

Abstract

fetched live from OpenAlex

Feedback is a cornerstone of human development. Not surprisingly, it plays a vital role in team development. However, the literature examining the specific role of feedback in virtual team effectiveness remains scattered. To improve our understanding of feedback in virtual teams, we identified 59 studies that examine how different feedback characteristics (content, source, and level) impact virtual team effectiveness. Our findings suggest that virtual teams benefit particularly from feedback that (a) combines performance-related information with information on team processes and/or psychological states, (b) stems from an objective source, and (c) targets the team as a whole. By integrating the existing knowledge, we point researchers in the direction of the most pressing research needs, as well as the practices that are most likely to pay off when designing feedback interventions in 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.

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)0.003
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 score0.463
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.052
GPT teacher head0.377
Teacher spread0.325 · 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