Job scope, affective commitment, and turnover: The moderating role of growth need strength
Bibliographic record
Abstract
Using a sample of business alumni from multiple organizations ( N = 230), we examined the relationships of job scope to actual turnover, measured 15 months later, as mediated by affective commitment and moderated by growth need strength (which was operationalized through learning goal orientation, need for achievement, and proactive personality as first‐order factors). Moderated mediation analyses (Edwards & Lambert, 2007, Psychol. Methods , 12, 1–22) revealed that: (1) job scope's relationship to commitment was stronger at high levels of growth need strength; (2) the indirect effect of job scope on turnover was stronger at high levels of growth need strength; and (3) growth need strength had a residual, positive relationship to turnover. We discuss the implications of these findings for our understanding of how motivation‐related individual difference variables combine with job characteristics and commitment in explaining turnover decisions. Practitioner points Organizations should provide challenging job characteristics to employees with high growth need as this may lead to increased affective commitment and lower turnover rates. For employees with weak growth needs, organizations may build climates for learning, achievement, and self‐initiative as this may create the conditions for the emergence of commitment and reduce turnover.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".