The link between talent management, organizational commitment and turnover intention: A moderated mediation model
Bibliographic record
Abstract
It has been suggested that talent management (TM) has a direct and significant relationship with a number of employee outcomes. This is while the number of studies examining the process of TM leading to these outcomes are limited. Therefore, the main purpose of this study is to present a new model for analyzing processes of TM and its linkage with several employee and organizational outcomes that are organizational commitment and turnover intentions. Additionally, the present paper involves a mediating factor (P-O fit) alongside a moderating variable that is, Organizational Culture. A sample of 510 employees were selected from different banks located in Amman, Jordan. Mediation and moderation models were tested through structural equation modeling (SEM). The findings, being in consensus with previous studies, showed that TM has a linkage with both TI and Organizational Commitment. Mediating effect of P-O fit was shown with both aforementioned variables. In addition, the moderation effect of organizational culture on the relationship between TM and TI was found. The study contributes to the literature of the topic by providing a fit model to explain the linkage of TM and a number of organizational and employee outcomes. Bank managers can benefit by being aware and implying TM practices within their firms to further develop company advancements and attending to their employees based on new HRM trends.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".