TALENT MANAGEMENT AND ORGANIZATIONAL EFFICIENCY: EXPLORING THE MODERATING ROLE OF EMPLOYEE TURNOVER INTENTION IN THE PAKISTAN TELECOMMUNICATION SECTOR
Bibliographic record
Abstract
The high Employee Turnover Intention (ETO) is a prevalent issue in the contemporary world of business, which is, directly and indirectly affecting organizations. The TC of Pakistan is the fastest-growing sector; however, if Talent Management (TM) is not properly managed, it results in Employee Turnover Intention and reduces Organizational Efficiency (OE). This study is carried out to examine the impact of talent on OE and examine the moderating role of ETO in the Telecommunication sector (TC) of Pakistan. The study is carried out using structured questionnaires as distributed amongst 125 employees of cellular service providers working in franchises, regional offices and business centers operated in Lahore- the second- largest hub of mobile users in Pakistan. Regression and MODGRAPH were used to check the effect of moderating variable, ETO. The findings of the study indicate that managing talent has a significant role in improving OE. In addition, ETO moderates the relationship between TM and OE in the TC of Pakistan.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".