The Impact of Applying the Blue Ocean Strategy on the Achievement of a Competitive Advantage: A Field Study Conducted in the Jordanian Telecommunication Companies
Bibliographic record
Abstract
The present study aimed to identify the impact of applying the blue ocean strategy on the achievement of a competitive advantage in the Jordanian telecommunication companies. In order to achieve the study’s objectives, the descriptive analytical approach was adopted and a questionnaire was developed. After that, the questionnaire forms were distributed to the selected sample. The sample consists of one hundred (100) administrators working in Jordanian telecommunication companies. The researchers used descriptive statistical methods, as well as, linear regression analysis, that were done to test the study’s hypotheses and analyze the collected data. Several results were concluded. For instance, it was concluded that the blue ocean strategy dimensions are highly applied. It was also found that the elimination process significantly affects the achievement of a competitive advantage. It was also found that the reduction process significantly affects the achievement of a competitive advantage. In addition, it was found that the increasing process significantly affects the achievement of a competitive advantage. It was found that the innovation process significantly affects the achievement of a competitive advantage. In the light of the aforementioned results, several recommendations were suggested by the researchers. For instance, the researchers recommend providing customers with service guarantees by the Jordanian telecommunication companies. In addition, the researchers recommend overcoming the obstacles that hinder the application of the blue ocean strategy by the senior management. The researchers recommend utilizing the blue ocean strategy by companies and avoiding negative competition. The researchers also recommend making significant strategic changes in an ongoing manner. Such changes should be provided with support by the board of directors.
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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 | 0.002 | 0.000 |
| 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 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".