EMPLOYEE RETENTION STRATEGIES IN INFORMATION TECHNOLOGY COMPANIES: A CASE STUDY IN HYDERABAD
Bibliographic record
Abstract
Given the volatility and uncertainty of today's economic climate, it is crucial thatIT companies develop and implement effective personnel management strategies aimed atretaining their most valuable employees. The high rate of employee turnover that had becomesuch a problem for successful IT organizations has begun to level out as the resignation wavesubsides. Most of India's top IT firms, including Infosys, TCS, HCL, and Tech Mahindra,reported lower attrition rates in the fiscal year's second quarter of 2022 (17-18% vs. 22-25%).However, even this reduction represents a significant increase in employee departures fromthe company when compared to rates seen before the global financial crisis: "the fear oflayoffs really isn't affecting the talented as the one who knows their worth,once decided tomove to It is clear that even in economic moments, companies regard talent Exodus andround robin as tactics to keep their talents for a longer amount of time in order to have aCompetitive advantage, as Mohit Joshi, president of Infosys, will take over as CEO and MDof Tech Mahindra from December 20-23. In January 2022, Ravi Kumar, president of Infosys,will succeed Brain Hempries as CEO and MD of Cognizant.Mohit Joshi, Infosys president,takes charge as CEO and MD of Tech Mahindra from December 20–23, so it is evident thateven in a recession period, the Talent Exodus is seen in the IT sector, which mean attrition isstill a persistent issue of IT companies, which can be driven by the best employee retentionstrategies.In this paper, an Empirical reviews of various reviews of literature and researchwork done on employee retention strategies in IT sector can be seen, the paper studies aboutthe perception of employees on retention strategies.
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How this classification was reachedexpand
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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.005 |
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; both teacher heads agree on what is shown here.
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".