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Record W2513095860 · doi:10.3389/fpsyg.2016.01336

Effects of Team Emotional Authenticity on Virtual Team Performance

2016· article· en· W2513095860 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFrontiers in Psychology · 2016
Typearticle
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsMcMaster University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyTeamworkAffect (linguistics)Context (archaeology)Virtual teamTeam compositionTeam effectivenessPsychological safetySocial psychologyApplied psychologyTone (literature)Structural equation modelingKnowledge managementCommunicationComputer science

Abstract

fetched live from OpenAlex

Members of virtual teams lack many of the visual or auditory cues that are usually used as the basis for impressions about fellow team members. We focus on the effects of the impressions formed in this context, and use social exchange theory to understand how these impressions affect team performance. Our pilot study, using content analysis (n = 191 students), suggested that most individuals believe that they can assess others' emotional authenticity in online settings by focusing on the content and tone of the messages. Our quantitative study examined the effects of these assessments. Structural equation modeling (SEM) analysis (n = 81 student teams) suggested that team-level trust and teamwork behaviors mediate the relationship between team emotional authenticity and team performance, and illuminate the importance of team emotional authenticity for team processes and outcomes.

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)0.000
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 score0.482
Threshold uncertainty score0.650

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.008
GPT teacher head0.281
Teacher spread0.272 · 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