The Development of Competitive Advantages of Brand in ihe Automotive Industry (Case Study: Pars Khodro Co)
Bibliographic record
Abstract
Achieving competitive advantage in the automobile manufacturing companies in the world to remain stable in the current atmosphere in consideration of complex and competitive environment of today's markets is considered as one of the critical issues in manufacturing companies. The main problem in this research is to identify the competitive advantages of brand and model their competitive advantage in the automotive industry. In this regard, the study of theoretical foundations of research in the field of competitive advantages of brand, components have been identified and by using Delphi techniques and structures final interviews added to Inventory and native competitive advantages of brand models in the automotive industry has been identified. Data were analyzed using SPSS and Smart PLS software. The results of data analysis totally indicated in the answer to this question could be deduced that in the final version is extracted using Smart PLS software, was observed that due to the three outcome measures 0.01, 0.25 and 0.36, as quantities of weak, medium and strong for GOF, of the 0.60 show is a fitting strong model that indicates that fitted the pattern of competitive advantage brand.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".