Understanding the Turnover Intentions of Information Technology Personnel
Why this work is in the frame
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Bibliographic record
Abstract
Most of the studies on IT personnel turnover intentions were carried out in the developed countries. Only a few researchers have focused on developing countries. The authors' study makes a comparative study of IT personnel turnover intentions in two sub-Saharan African countries (Botswana and Nigeria) using the Igbaria and Greenhaus turnover model. The intent was to find out if the same model elements affect turnover intentions in the two countries. The results show that demographic variables (age and length of service), the role stressors (role ambiguity and role conflict), the career related variables (growth opportunity, supervisor support and external career opportunities), job satisfaction and career satisfaction have direct effect on turnover intentions in these two developing countries, while other affectors in the research model do not hold equally for the two countries, except for growth opportunity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.007 |
| 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