Investigation the impact of outsourcing on competitive advantages' creation by considering Porter's model; Case study: Zamyad Company
Bibliographic record
Abstract
Competitive advantage is an important factor in boosting companies' success and is considered more emphatically in management and strategic marketing literature in recent years.There are many different ideas about effective factors in creation of competitive advantages.Also fast rate of change in business, is forcing CEOs to utilize some strategies, which have the best impact on current organizational circumstances and the future trend of investigation in organizational trades.Outsourcing is one of the best strategies, which are widely utilized by CEOs in different organizations.Many managers believe that outsourcing is the solitary way for preserving the balance of organization in 21 century.Based on Porter competitive advantage model, there are three strategies, which lead a company to reach competitive advantage.These strategies are cost leadership, differentiation strategy and segmentation strategy .In this article, we are investigating outsourcing effects on creation of competitive advantages through Porter model in an automotive factory in Iran.We design a questionnaire for gathering necessary information about the role of outsourcing in creation of different strategies as competitive advantages in managers' point of view.We analyze the questionnaires and implement a goodness of fit test to recognize the distribution of data and the statistical method.Preliminary results show that nonparametric statistic methods can be utilized for testing our hypothesis.We use a Wilcoxon test to consider the null hypothesis and a Friedman test to estimate the rank of means.Our findings verify an undeniable effect of outsourcing on creation of competitive advantage and the ranking list is presented.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".