Designing a Competitive Advantage Model Through Dynamic Capabilities and Differentiation Approach for Iranian Knowledge-based Companies
Bibliographic record
Abstract
The worldwide competitive struggles in high-tech environment such as knowledge-based companies have featured the necessity to infer how competitive advantage is gained. Dynamic capabilities as the origin of competitive advantage emphasize the changing character of the environment and nature of future competition, acceleration in innovation growth, and the main role of strategic management in adaption, integration, and reconfiguration of organizational skills, resources, and working competences toward shifting environment. The recent inquiry is based on an interpretive paradigm and an inductive approach. The qualitative part of the research is conducted with exploratory purpose through grounded theory strategy. In quantitative part of study, it is continued with explanatory and descriptive purposes through survey and correlational research strategies. The probe concepts and variables are analyzed in 30 top knowledge-based companies located at growth centers of 6 high-ranking universities of Iran working on electronics and informatics field. The results of the research indicate that achieving knowledge-centricity main phenomenon through dynamic capabilities requires presence of value creation on the basis of resource orientation together with competences. The knowledge-based companies can reach the summit and sustainable success in the knowledge-centricity main phenomenon when they consider two kinds of specialized paths related to the differentiation strategies and knowledge-based strategies. Along with these two paths, contextual factors of environmental cognition, knowledge management and knowledge approaches, and the intervening conditions of branding and brand management, and strategic agility are identified that have a positive and significant effect on both strategies. The final designed model explicates how to gain competitive advantage for the studied companies through dynamic capabilities with regard to the differentiation and the knowledge-based approaches.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".