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Record W2030149026 · doi:10.1002/cjas.90

Proactive personality and work performance in China: The moderating effects of emotional exhaustion and perceived safety climate

2009· article· en· W2030149026 on OpenAlexaffvenue
Vishwanath V. Baba, Louise Tourigny, Xiaoyun Wang, Weimin Liu

Bibliographic record

VenueCanadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l Administration · 2009
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsUniversity of ManitobaMcMaster University
Fundersnot available
KeywordsPsychologyEmotional exhaustionPersonalityOrganizational citizenship behaviorSocial psychologyApplied psychologyJob performanceProactivityWork (physics)BurnoutJob satisfactionOrganizational commitmentClinical psychology

Abstract

fetched live from OpenAlex

Abstract Grounded in the interactionist paradigm, this study shows that emotional exhaustion and perceived safety climate constitute important moderators of the relationship between proactive personality and work performance. More specifically, the study analyzes the relationship between proactive personality and its behavioural outcomes—organizational citizenship behaviour (OCB) and job performance—and investigates the interactive effects of emotional exhaustion and perceived safety climate. The study involves 485 Chinese airline employees including pilots, flight attendants, engineers, and service employees. Proactive personality positively predicted OCB and individual performance. Emotional exhaustion and perceived safety climate moderated the relationship between proactive personality and individual performance both independently and jointly. Implications of the findings for future research are discussed. Copyright © 2009 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)0.002
metaresearch head score (Gemma)0.003
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.112
GPT teacher head0.413
Teacher spread0.301 · 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

Citations99
Published2009
Admission routes2
Has abstractyes

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