Work and well-being: collective and individual self-concept, job commitment, citizenship behavior, and autonomy as predictors of overall life satisfaction
Bibliographic record
Abstract
A job may contribute to overall life satisfaction (LS) when it meets basic psychological needs. This study examined aspects of one's work (organizational commitment, citizenship behavior and autonomy), and individual differences in self-concept (collective versus individual), as predictors of overall LS. 295 employees working at a variety of jobs completed questionnaires online. Results showed that higher collective self-concept predicted greater LS; this was partially mediated by affective job commitment, work autonomy and altruistic citizenship behaviors, all of which also independently predicted greater LS. Higher individual self-concept was also a significant predictor of LS, partially mediated by compliance citizenship behaviors. These results suggest that when work fulfills a need for connectedness (i.e., for people with a collective self-concept), autonomy, and when we feel emotionally committed to our job, and go beyond what it requires, our overall LS is higher. These results clarify some positive ways that our work contributes to overall LS, but important questions remain for future research.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".