Influences of Financial and Non-Financial Compensation on Employees’ Turnover Intention in the Energy Sector: The Case of Aramco IPO
Bibliographic record
Abstract
The purpose of this research is to identify the effects of financial and non-financial compensation on employees’ turnover intention at Saudi Aramco after an initial public offering (IPO). A questionnaire was used for collecting data from 142 participants, and a simple linear regression model was used to analyse the survey results. This study revealed that the respondents agree that financial compensation is an important factor in their tendency to continue working at their company and that training is another important factor that motivates them to stay. Further, the results show that financial compensation affects employees’ turnover intentions, training positively affects their retention at their respective company, and promotion policies correlate with their turnover intentions. The study recommends that companies in the energy sector should give more attention to financial compensation, training, and promotion to motivate employees to remain in their companies. According to the research results, there are positive relationships between retention and continuing in a company based on the independent variables of financial compensation, training, and promotion.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".