Investigation into Factors Influencing Employee Retention Among IT Professionals: A Perspective from India
Bibliographic record
Abstract
Employee retention poses a significant challenge for IT organizations in India, where skilled professionals are in high demand both domestically and internationally. The departure of technocrats in pursuit of better opportunities threatens the stability and productivity of these organizations, particularly in the face of economic uncertainty and fierce competition. To address this issue, effective retention strategies are crucial. This study adopts a holistic approach to investigate the factors influencing employee turnover in Indian IT and multinational companies, as perceived by HR managers. The research aims to identify the reasons for employee attrition, factors contributing to retention, attitudes toward work, work relationships, and basic expectations from the organization. Furthermore, the study seeks to determine if there are any significant differences in responses between IT professionals employed in Indian IT companies versus multinational corporations. Analyzing data collected from 30 IT professionals, the study found no significant difference in responses between these types of companies. However, differences were observed based on certain demographic factors such as total experience, position, and participation in sponsored certification programs. The findings of this study are expected to assist HR managers in developing tailored retention strategies to mitigate attrition rates within their respective organizations.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".