The effects of personality on job satisfaction and life satisfaction: A meta-analytic investigation accounting for bandwidth–fidelity and commensurability
Bibliographic record
Abstract
To what extent do employees’ personality traits shape their perceptions of job and life satisfaction? To answer this question, we conducted the largest meta-analysis on the topic to date, summarizing a total of 12,682 correlations among combinations of personality, job satisfaction and life satisfaction. We also sought to refine previous meta-analytic estimates by comparing the effects of personality facets to broad trait domains, while controlling for commensurability of personality measures. The results showed that the Big Five personality traits accounted for about 10% of the variance in job satisfaction, which in turn accounted for 13% of the variance in life satisfaction. Compared with the broad trait domains, personality facets typically accounted for twice as much variance in life satisfaction, with only a minor increase for job satisfaction, which contradicts the typical bandwidth–fidelity heuristic. The results also provided support for a trickle-down or top-down effect, where dispositions affect perceptions of life satisfaction, which then influenced the more specific subdomain of job satisfaction. The results have important implications for researchers and practitioners, suggesting that information is lost when personality facets are overlooked, and that educational and workplace interventions could enhance perceptions of satisfaction for those prone to lower levels of subjective well-being.
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 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 | 0.054 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.038 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| 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".