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Record W4212808775 · doi:10.5093/jwop2022a3

Cross-cultural Evidence of the Relationship between Subjective Well-being and Job Performance: A Meta-analysis

2022· article· es· W4212808775 on OpenAlexaboutno aff
Jesús F. Salgado, Silvia Moscoso

Bibliographic record

VenueJournal of Work and Organizational Psychology · 2022
Typearticle
Languagees
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsnot available
Fundersnot available
KeywordsModerationMeta-analysisPsychologyCross-culturalSubjective well-beingJob performanceDemographySocial psychologyJob satisfactionHappinessMedicinePolitical scienceSociology

Abstract

fetched live from OpenAlex

This meta-analysis examined the differences across countries/regions, and the moderator effects of the study type (cross-sectional vs. longitudinal) on the SWB-job performance relationship. The database consists of 78 independent samples (N = 18,853), located through electronic and manual searches. The results showed that overall SWB (ρ = .37), cognitive SWB (ρ = .27), and affective SWB (ρ = .37) are predictors of job performance. Evidence of cross-cultural effects showed that the magnitude of the SWB-job performance relationship was larger in the Asia-Pacific region than in Europe and the US-Canada region (Asia-Pacific ρ = .41, Europe ρ = .33, USA ρ = .23). Moderator analyses indicated that, on average, cross-sectional (concurrent) and longitudinal (predictive) studies showed similar validity (ρ = .33 vs. ρ = .32). Lastly, we discuss the main contributions, and some practical implications and some limitations of the study are mentioned.

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)0.022
metaresearch head score (Gemma)0.046
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.046
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0110.032
Bibliometrics0.0070.006
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.002
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 head0.085
GPT teacher head0.390
Teacher spread0.305 · 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 designMeta-analysis
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

Citations25
Published2022
Admission routes1
Has abstractyes

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