Career Competencies and Job Performance of Saudi Employees in Tourism Industry: Intelligent Career Model
Bibliographic record
Abstract
Tourism industry is one of the most growing sectors that contributes significantly to Gross World Product (GWP), employment opportunities, and local culture. Saudi Arabia has witnessed a growing number of hotel projects of new international chains and domestics hotels. Studies relevant to career competencies topics are relatively scarce among the first-line staff in Saudi’s hotels industry. This study seeks to examine the relationship between career competencies and job performance among frontline staffs at luxury, mid and upper mid-scale hotels in Riyadh and Makkah regions of Saudi Arabia. This study used online questionnaire method and was sent to 700 different hotels with a response rate of 76% (n=499). Partial least squares structural equation modeling (PLS-SEM) was used for data analysis. Results showed that reflective, communicative, and behavioral career competencies have a statistically significant positive influence on individual work performance with values of (B=0.3, p<0.01), (B=0.34, p<0.01), and (B=0.21, p<0.01) respectively. A number of practical implications along with the research limitations were discussed.
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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 | 0.002 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".