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Record W4399475720 · doi:10.1108/jgm-02-2024-0006

Does workplace incivility trigger the intention to self-initiate expatriation? An investigation among young Tunisian physicians

2024· article· en· W4399475720 on OpenAlexaff
Emna Gara Bach Ouerdian, Khadija Gaha, Nizar Mansour

Bibliographic record

VenueJournal of Global Mobility The Home of Expatriate Management Research · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsUniversité Sainte-Anne
Fundersnot available
KeywordsIncivilityPsychologySocial psychology

Abstract

fetched live from OpenAlex

Purpose This paper aims to examine the relationship between workplace incivility and the intention to self-initiate expatriation and whether this relationship is mediated by affective organizational commitment. It also explores the moderating role of career commitment in this proposed model. Design/methodology/approach The data were collected using a questionnaire among 145 young physicians from Tunisian hospitals. Hypotheses are tested using the PROCESS macro (models 4 and 7) in SPSS. Findings Workplace incivility is negatively related to affective organizational commitment, which in turn is related to the intention to self-initiate expatriation. Furthermore, career commitment moderates the indirect effect of workplace incivility on expatriate intention through affective organizational commitment. Specifically, when career commitment is high, the indirect effect on the intention to self-initiate expatriation is stronger. Originality/value This is one of the first studies to examine the indirect influence of workplace incivility on the intention to self-initiate expatriation. Moreover, it furthers our understanding of a contingent factor that influences this indirect effect.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.040
GPT teacher head0.386
Teacher spread0.346 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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