Mapping the relationship between proactive behavior and talent management practices: The mediating role of organizational commitment
Bibliographic record
Abstract
In a diverse and modern organization with high extent of competitiveness within the market, maintaining high performance is of necessity. Talent management practices, when implied and used properly can significantly contribute to an organizations’ degree of overall performance as it has been noted throughout the literature. Employees and individuals seeking professional careers are required to cope with fast-changing environments of their workplaces. The need to constantly improve oneself is a dire one. Current research paper analyzes mediation effect of organizational commitment on the relationship between proactive personality and talent management practices from employee perspective of university academic and administrative staff. Mediation regression analysis (PROCESS) has been used to analyze the gathered data from universities located in North Cyprus, and the accumulated results show a full mediation effect from organizational commitment on the aforementioned relationship. The study contributes to the literature through expansion of proposed model in context of talent management and proactive personality as well as analytical method alongside context of academia. Furthermore, this study provides tangible implications, which can be beneficial for university decision-makers.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".