Do vacations and parental leave reduce voluntary turnover? A study of organizations in the ICT sector in Canada
Bibliographic record
Abstract
Purpose This study examines the link between vacations, parental leave and voluntary turnover among Canadian organizations in the Information and Communications Technology (ICT) sector. Design/methodology/approach The empirical analysis is carried out using firm-level data sourced from a survey that was completed by HR managers of 125 ICT firms operating in the province of Quebec (Canada).The organizational voluntary turnover rate was used and was obtained by dividing the number of employees who voluntarily quit an organization within the last year by the total number of its employees. Based on ordinary least squared estimates, results were generated by regressing voluntary turnover rate on vacation and parental leave. Findings Vacation, operationalized as the average number of annual vacation days, is negatively and significantly associated with the voluntary turnover rate of the ICT organizations surveyed. Parental leave, operationalized as the percentage of salary reimbursed during parental leave, does not significantly reduce voluntary turnover in the ICT organizations surveyed. Practical implications In light of the results of this study, if organizations in the ICT sector, in Canada or abroad, desire to reduce voluntary turnover, compensating employees through the use of additional vacation days appears to be a viable approach. Originality/value This research constitutes an empirical test of the link between turnover and two compensation practices adopted by firms. To our knowledge, there is no prior scientific evidence on that subject in the Canadian ICT sector.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".