Turnover in Japanese IT Professionals: Antecendants and Nuances
Bibliographic record
Abstract
The Japanese information technology (IT) workplace is unique compared to that of other nations. IT represents a large sector of the country’s economy, and organizations need to develop proactive approaches to retain their IT workforce. In order to manage employee turnover, they need to understand the distinctive factors influencing employee turnover intention, as turnover intention is known to be a reliable predictor of actual turnover. In this study, a model was constructed and tested with data collected from 284 Japanese IT professionals. Our findings show that the effects of work exhaustion, personal accomplishment, and friendship networks on turnover intention are fully mediated through job satisfaction. Work-home conflict has no impact on job satisfaction. The strength of the relationships is stronger for younger than for older organizations. Furthermore, individualistic factors (i.e., work exhaustion and personal accomplishment) have a stronger impact on job satisfaction than collectivistic factors (i.e., work-home conflict and friendship networks). These results show the fragility of the notion of long-term employment, which is supposed to be embraced within the entire Japanese work culture.
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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 | 0.003 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".