Work From Home And Its Influence On Turnover Intentions Among IT Professionals
Bibliographic record
Abstract
The global shift towards remote work, accelerated by the COVID-19 pandemic, has fundamentally transformed workplace dynamics, particularly within the IT sector. This study investigates the influence of work from home (WFH) on turnover intentions among IT professionals in the Tri-City region. By analyzing data from 200 respondents, the study examines the roles of job satisfaction, organizational support, work-life balance, and career development opportunities in shaping employees' intentions to stay with or leave their current organizations. The findings indicate that higher job satisfaction, robust organizational support, and effective work-life balance are significantly associated with lower turnover intentions, while limited career development opportunities contribute to higher turnover intentions. These insights underscore the importance of a holistic approach to managing remote work environments to enhance employee retention. The study's implications are vital for HR managers and organizational leaders aiming to optimize remote work practices and maintain a committed and productive workforce.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".