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Record W4389092320 · doi:10.1177/00218863231216724

Team Readiness to Change: Reflexivity, Tenure, and Vision in Play

2023· article· en· W4389092320 on OpenAlexaffabout
Patrick Groulx, Kevin Johnson, Jean‐François Harvey

Bibliographic record

VenueThe Journal of Applied Behavioral Science · 2023
Typearticle
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsReflexivityTeam effectivenessOrganizational changePsychological safetyPsychologyTeam compositionPublic relationsKnowledge managementBusinessSociologySocial psychologyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

How can teams make sense of a complex organizational transformation and be ready to change? These questions must be addressed as organizations turn towards team-based structures to become more reactive. During organizational transformations, we argue team reflexivity enables team members to share interpretations of changes, leading to the development of team change vision—the overarching sense of direction for simultaneous change initiatives. We further argue that team reflexivity is more effective for teams with greater team tenure dispersion and additive team tenure. We tested and found support for our theory using time-lagged, survey-based data from 70 teams at a Canadian governmental organization. Overall, our study contributes to team readiness to change literature by identifying team reflexivity as a central information-processing activity enabling teams to develop a team change vision during an organizational transformation and by clarifying the effect of team tenure on such activity.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.037
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.007
Scholarly communication0.0060.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.077
GPT teacher head0.449
Teacher spread0.371 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2023
Admission routes2
Has abstractyes

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