EFFECT OF TALENT MANAGEMENT PRACTICES AND ORGANISATIONAL PERFORMANCE ON EMPLOYEE RETENTION: EVIDENCE FROM INDIAN IT FIRMS
Bibliographic record
Abstract
Couple of years back, home-grown e-commerce player Snapdeal made a claim that India lacked talented programmers to meet their needs. This assertion reemphasized the importance of talented employees and their skills in the success of any organization. Understandably, a lot of research efforts have been made in last two decades to tackle issues related to employee retention. This study examined the role of talent management practices and organizational performance on employee retention in the Indian IT sector. Based on literature review, three leading hypotheses were formed. Primary data was collected from 33 IT firms, leading to a total of 68 responses. Based on statistical analysis using SPSS 21.0, correlations between the variables were studied. Additionally, regression was also performed between the dependent and independent constructs. The results revealed that significant relationship was found between talent management and employee retention. On the other hand, organizational performance, on its own, didn’t emerge as a driving factor for employee retention. However, along with talent management practices, organization performance was found to have significant effect on employee retention.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".