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The Interplay between Learning Orientation, Openness and Psychological Safety in Team Learning

2018· article· en· W2813343601 on OpenAlexaff
Jean‐François Harvey, Kevin Johnson, Kathryn S. Roloff

Bibliographic record

VenueAcademy of Management Proceedings · 2018
Typearticle
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsPsychological safetyTeam learningPsychologyOpenness to experienceMediationTeam compositionTeam effectivenessModerated mediationOrientation (vector space)TeamworkSocial psychologyApplied psychologyCooperative learningKnowledge managementPedagogyOpen learningTeaching methodManagementComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Team learning has long been a central interest in team scholarship. Within this work, a number of emergent states have been considered in their influence on team effectiveness. In particular, team learning orientation is known to be positively related to team learning. However, it is less understood exactly how team learning orientation fosters team learning or whether there is conceptual overlap between team learning orientation and other emergent states related to team learning such as team psychological safety. To explore these questions, we designed a time-lagged, survey-based study of teams in the sales division of a large financial services firm, and tested a moderated mediation model of team learning that includes team learning orientation, team openness, and team psychological safety. Our results demonstrates that the relationship between team learning orientation on team learning is mediated by team psychological safety and further, that the relationship between team learning orientation and team psychological safety is moderated by the level of team openness. The mediation effect was only present when team openness was low, but not when it was high. Therefore, we reveal some initial patterns of interaction and discrimination among key team emergent states that are related to team learning.

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 score0.194
Threshold uncertainty score0.412

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 integrity0.0000.001
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 head0.025
GPT teacher head0.367
Teacher spread0.342 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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