EFFECT OF TALENT MANAGEMENT PRACTICES AND ORGANISATIONAL PERFORMANCE ON EMPLOYEE RETENTION: EVIDENCE FROM INDIAN IT FIRMS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it