Cross-cultural Evidence of the Relationship between Subjective Well-being and Job Performance: A Meta-analysis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.032 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".