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Record W2136543703 · doi:10.1002/cjas.1277

A multilevel perspective on the relationship between interpersonal justice and negative feedback‐seeking behaviour

2014· article· en· W2136543703 on OpenAlexvenueno aff
Aichia Chuang, Chun‐Yang Lee, Chi‐Tai Shen

Bibliographic record

VenueCanadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l Administration · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsSupervisorPerspective (graphical)Interpersonal communicationSocial psychologyPsychologyEconomic JusticeMediationMultilevel modelField (mathematics)SociologyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Abstract Drawing on social information processing theory, this study uses a multilevel design to integrate the literature on organizational justice with the literature on feedback‐seeking behaviour. Results from a laboratory study with data involving 690 employees showed that individual‐level interpersonal justice was related to employee negative feedback‐seeking behaviour (NFSB) via the mediation of trust in the supervisor. Multilevel analysis of the follow‐up field study with data involving 390 employees from 46 teams confirmed the findings of the laboratory study and indicated that team‐level interpersonal justice was associated with NFSB through a supportive climate. Also, team‐level supervisor support climate was positively related to individual‐level trust in the supervisor. The paper discusses managerial implications of these findings and suggests directions for future research. Copyright © 2014 ASAC. Published by John Wiley & Sons, Ltd.

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)0.003
metaresearch head score (Gemma)0.014
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 score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.125
GPT teacher head0.326
Teacher spread0.201 · 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

Citations15
Published2014
Admission routes1
Has abstractyes

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