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Record W1992290108 · doi:10.1080/13032917.2013.804425

Antecedents and consequences of employee voice behaviour among front-line employees in Turkish hotels

2013· article· en· W1992290108 on OpenAlexaff
Mustafa Koyuncu, Ronald J. Burke, Lisa Fixenbaum, Yasemin Tekin

Bibliographic record

VenueAnatolia · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsYork University
Fundersnot available
KeywordsTurkishEmployee voiceFront lineHospitalityHospitality industryPsychologyJob satisfactionWork (physics)Front (military)BusinessMarketingSocial psychologyTourismPolitical science

Abstract

fetched live from OpenAlex

This paper explores antecedents and consequences of employee voice behaviour among front-line employees working in the hospitality industry in Turkey. Data were collected from 594 front-line service employees working in 15 top-quality hotels using anonymously completed questionnaires, a 59% response rate. Respondents indicated a generally moderate level of voice behaviour. Personal demographic characteristics and work situation characteristics had few and inconsistent relationships with employee voice behaviour. Males and females were equally likely to engage in voice behaviours. Two aspects of workplace culture were associated with higher levels of employee voice behaviour. Employees engaging in more voice behaviour were also more job-satisfied, more work-engaged, and more likely to remain with their employer.

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)0.002
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.241
Teacher spread0.227 · 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

Citations20
Published2013
Admission routes1
Has abstractyes

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