Étude longitudinale de l’impact de la satisfaction envers les avantages sociaux novateurs sur l’intention de rester chez les travailleurs du secteur des technologies de l’information et des communications
Bibliographic record
Abstract
The objective of this thesis is to understand the impact of satisfaction with innovative benefits on the intention to stay of the information and communications technology industry (ICT) workers. In order to investigate this question, a general research hypothesis was presented based on a literature review and on Blau’s social exchange theory (1964) and Maslow’s theory of needs (1943). The general research hypothesis states that satisfaction with innovative benefits increases intention to stay through time. The data used in this thesis were collected as part of a larger research on the relationships between compensation, training and skills development and attracting and retaining key employees. The longitudinal data come from an office located in Montreal of a major international company from the ICT sector. The study population consists of workers newly hired between April 1st, 2009 and September 30th, 2010. The results confirm the research hypothesis showing that satisfaction with innovative benefits increase intention to stay through time. Among the various innovative benefits studied, the results indicate that it is the satisfaction with the gym at work that best predicts intention to stay of workers. Other innovative benefits significantly related to intention to stay are the games library, the lounge, the medical clinic and the library in the workplace. Also, longitudinal analyses reveal that it is mainly the differences between the individual characteristics of the employees who best explain intention to stay than the differences across time of the same worker. This thesis concludes by discussing the best way for industrial relations managers to use the results in order to retain their employees. Then, the limits of the study and some directions for further research are also presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".