The Impact of Dynamic Capabilities on Sustainable Competitive Advantage in the Pharmaceutical Sector in Egypt
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".