Comparing Job Satisfaction between the Genders in Saudi Telecom Sector: Optimizing Employee Input
Bibliographic record
Abstract
Growth and development of infrastructural sectors is one of the keynotes of Vision 2030 which envisages a country well on the road to internationalization by the end of the next decade. Development of the telecom sector, which saw a slump in early 2018 and later picked up with growth in the number of mobile and high-speed internet users, is also one of the objectives. The reason is the large number of jobs this sector is likely to generate. In a highly segregated society like that of Saudi Arabia, it is imperative to evaluate social, educational and occupational setups to ensure parity of opportunities for the genders. This study is also directed at such an objective, identifying trends in job satisfaction among female and male Saudi employees in the telecommunications and information technology sector in the Kingdom of Saudi Arabia. The study selected seven variables, validated previously to assess the perception of males and females employed in the sector and designed a questionnaire for the purpose. The sample comprised two hundred and thirty participants. Post statistical analysis, the results indicated that there were statistically significant differences between the average job satisfaction among male and female employees in this sector. Overall, the average job satisfaction among females was higher than that of males. Females also showed an increase in the average satisfaction concerning financial factors and relationships within the work environment as well as career prospects. By contrast, job satisfaction among males was higher with respect to work elements relates to existing job systems and educational field. The study offers suitable recommendations to ensure greater job satisfaction and thereby promote optimum utilization of the human resource involved in the sector.
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How this classification was reachedexpand
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".