The Impact of Dynamic Capabilities on Sustainable Competitive Advantage in the Pharmaceutical Sector in Egypt
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study examines the relationship between dynamic capabilities (experience, routine, skills, firm characteristics, knowledge and technology) and competitive advantage sustainability in the Egyptian pharmaceutical sector. The data was collected using primary and secondary data sources. Primary data was collected from questionnaires distributed to 160 top managers in 20 pharmaceutical firms. The secondary data about pharmaceutical firms like rankings, revenues and market share was collected from external sources such as Intercontinental Marketing Service (IMS). The questionnaires examine six independent variables based on a five-scale Likert scale. The methodology used in the study is non-probability sampling (judgmental sampling), Cronbach’s alpha reliability coefficient and Chi-square tests. The results support the notion that there is a significant relationship between four of the six dynamic capabilities (experience, skills, firm characteristics and knowledge) and the competitive advantage sustainability for pharmaceutical firms in Egypt. Designing the questionnaire and formulating the questions to target the required field was challenging, given that the topic is dynamic and the business scene in Egypt has witnessed drastic political changes since January 2011. The study should assist pharmaceutical companies in Egypt in directing their investments properly and in determining the weaknesses in their dynamic capabilities that need to be addressed.
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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 | 0.004 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it