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Record W2599377818 · doi:10.5539/ibr.v10n4p131

The Moderating Role of Employees’ Humor Styles on the Relationship between Job Stress and Emotional Exhaustion

2017· article· en· W2599377818 on OpenAlexvenueno aff
Zeynep Oktuğ

Bibliographic record

VenueInternational Business Research · 2017
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyEmotional exhaustionJob stressSocial psychologyMultilevel modelEmotional laborEmotional well-beingModerationStress (linguistics)Coping (psychology)Developmental psychologyBurnoutClinical psychologyJob satisfaction

Abstract

fetched live from OpenAlex

In today’s work conditions, job stress and emotional exhaustion are serious threats for the health of employees. Previous research suggests a relationship between job stress and emotional exhaustion. The way individuals use humor has been associated with different coping strategies. The aim of this study is to investigate the moderating role of employees’ humor styles on the relationship between job stress and emotional exhaustion. 116 participants completed self-reported measures assessing their job stress, emotional exhaustion and humor styles. For data analyses a series of hierarchical moderated regression analyses were conducted. The findings show that self-enhancing and self-defeating humor styles have moderating effects on the relationship between job stress and emotional exhaustion. As the level of self-enhancing humor increases, the effect of job stress on emotional exhaustion is attenuated, on the other hand, as the level of self-defeating humor increases, the effect of job stress on emotional exhaustion is intensified. Findings regarding the effects of employees’ humor styles are discussed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.436
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.228
GPT teacher head0.477
Teacher spread0.249 · 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 teacher head, not a consensus.

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

Citations9
Published2017
Admission routes1
Has abstractyes

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