The relationship between human resource development factors, career growth and turnover intention: The mediating role of organizational commitment
Bibliographic record
Abstract
Retaining the best employees is of high concern for most organizations and this issue has become a significant focus of attention for many researchers. For this reason, this paper discusses different factors which influence the employee turnover intention-behavior in the organization, specifically to examine the effect of salary, performance appraisal, training & development and career growth on turnover intention. In addition, based on the social exchange theory this paper explains the mediating role of organizational commitment in the relationship between human resource development factors, career growth and turnover intention. A cross sectional, survey data study is undertaken to investigate the relationships in a sample of 270 full time faculty members employed in different private universities of Pakistan. Partial Least Square two step path modeling is used to test the direct and the indirect hypothesis of the study. The results of PLS (SEM) path modeling reveal that human resource development factors specially salary and performance appraisal were negatively associated with turnover intention. In addition, the results also indicate that career growth had significant relationships with turnover intention. Moreover, out of four dimensions of career growth, only two dimensions, namely promotion speed and remuneration growth, have strong influence on turnover intention. Finally, in terms of organizational commitment as mediating variable between the relationships of salary, performance appraisal, career growth and turnover intention, four out of six variables indicate partial mediation including career growth (career goal progress), career growth (promotion speed), career growth (remuneration growth) and performance appraisal.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".