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Record W3050962077 · doi:10.1108/pr-05-2019-0263

Core self-evaluations associated with workaholism: the mediating role of perceived job demands

2020· article· en· W3050962077 on OpenAlexaff
Ying An, Xiaomin Sun, Kai Wang, Huijie Shi, Zhenzhen Liu, Yiming Zhu, Fang Luo

Bibliographic record

VenuePersonnel Review · 2020
Typearticle
Languageen
FieldPsychology
TopicWorkaholism, burnout, and well-being
Canadian institutionsQueen's University
Fundersnot available
KeywordsPsychologyCore self-evaluationsSocial psychologyExtant taxonLocus of controlMediationJob performanceJob attitudeApplied psychologyJob satisfaction

Abstract

fetched live from OpenAlex

Purpose Why do some employees choose to prolong their working hours excessively? The current study tested how core self-evaluations (CSEs) might lead to workaholism and how perceived job demands might mediate this relationship. Design/methodology/approach Insights from the extant literature underpin the hypotheses on how CSEs would affect the development of workaholism through perceived job demands. A sample of 421 working people in China completed the online surveys, and the mediation model was tested using Mplus 7.0 (Muthén and Muthén, 1998–2012). Findings This study found that different components of CSEs influence workaholism in different ways. Specifically, generalized self-efficacy positively predicts workaholism, whereas emotional stability negatively predicts workaholism. Moreover, most aspects of CSEs (generalized self-efficacy, emotional stability and locus of control) influence workaholism via perceived job demands, specifically via perceived workload but not via perceived job insecurity. Originality/value The current study is the first to explore how individuals' fundamental evaluations of themselves (i.e. CSEs) relate to workaholism. The results are helpful for the prevention and intervention of workaholism in organizations.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.680
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.064
GPT teacher head0.334
Teacher spread0.270 · 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 designQualitative
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

Citations13
Published2020
Admission routes1
Has abstractyes

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