Determinants of Competitive Advantages of Dates Exporting: An Applied Study on Saudi Arabia
Bibliographic record
Abstract
The study focus on testing the determinants of competitive advantage of dates marketing from Saudi Arabia through multi- regression model based on Porter’s diamond, which is determined the factor that affecting on competitiveness of nations in international marketing, such as factor conditions, demand conditions, related and supporting industries, and company strategy; structure; and rivalry. Our study selected the most competitive countries for Saudi Arabia in marketing dates in its markets (like Egypt, Iraq, and Tunisia). The results of study showed that the four determinants are significant and R square is high more than 95% in all equations this is agree with our assumptions, but the signs parameters of these determinants are different from our expectations specially with the quantity of production in Saudi Arabia which appear negative with the value of export of dates from KSA, that is because the consumption of dates in domestic market is high and it absorbs the high quality kind of dates, which is needed for external market. We tested also the same determinants for the competitive countries (Egypt, Iraq, and Tunisia); we found the same results, except Egypt, which have huge domestic demand that is effect on demand conditions in this country. Our study suggested more studies are needed for related and supporting industries of dates with this crop, to save data base in this field, and give more attention for quality of dates, packaging and prices for Saudi exporting of dates.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".