A STUDY ON EMPLOYEE RETENTION PRACTICES AND CHALLENGES IN HYDERABAD-BASED INFORMATION TECHNOLOGY COMPANIES
Bibliographic record
Abstract
It is critical for IT organisations to create and execute effective strategies for managing staff in order to hold on to their best workers in light of the current economic uncertainties and volatility. A levelling off of the high rate of personnel turnover has started as the resignation tsunami decreases, which was a major concern for successful IT businesses. Infosys, TCS, HCL, and Tech Mahindra—among India's leading IT companies—reported decreased attrition rates in the second quarter of the fiscal year of 2022 (17-18% vs. 22-25%). Though the number of employees leaving has decreased, it is still higher than the rates seen before the global financial crisis: "the fear of layoffs really isn't affecting the talented as the one who knows their worth, once decided to move to It is clear that even in economic moments, companies regard talent Exodus and round robin as tactics to keep their talents for a longer amount of time in order to have a competitive advantage," as Mohit Joshi, president of Infosys, will take over as CEO an After Brain Hempries steps down as CEO and MD of Cognisant in January 2022, Infosys president Ravi Kumar will take up the role.It is clear that the IT sector is experiencing the Talent Exodus even during recessionary times. Attrition is still a problem for IT companies, and the best strategies to retain employees can't stop Mohit Joshi, president of Infosys, from becoming CEO and MD of Tech Mahindra from December 2021 onwards.This study examines the viewpoints of workers about retention methods in the information technology industry and presents an empirical analysis of the relevant literature and research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".